{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#! pip install -r requirements.txt\n", "\n", "from src.ExperimentSettings import SETTINGS_DEFINITIONS, ExperimentSettings\n", "from src.Experiment import FCFLExperiment" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'id': 0,\n", " 'group_name': None,\n", " 'algorithm': 'FCFL',\n", " 'model': 'CNN',\n", " 'batch_size': 10,\n", " 'client_fraction': 0.2,\n", " 'dataset': 'FEMNIST',\n", " 'n_epochs': 3,\n", " 'learning_rate': 0.1,\n", " 'n_clients': 367,\n", " 'partition_strategy': 'non-iid',\n", " 'n_rounds': 1,\n", " 'seed': 0,\n", " 'target_accuracy': 0.8,\n", " 'job_dir': None,\n", " 'global_weight_delta_norm_threshold': None,\n", " 'max_client_weight_delta_norm_threshold': None,\n", " 'empirical_risk_approximation_error_bound': None,\n", " 'n_clusters': 4,\n", " 'n_pre_cluster_rounds': 1,\n", " 'cluster_algorithm': 'hierarchical',\n", " 'cluster_dist_metric': 'manhattan',\n", " 'cluster_hierarchical_linkage': 'average',\n", " 'cluster_hierarchical_dist_threshold': 1000.0,\n", " 'use_gpu': True}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get default setting values from SETTINGS_DEFINITIONS as a dict\n", "values = list(map(lambda name: SETTINGS_DEFINITIONS[name]['default'], SETTINGS_DEFINITIONS.keys()))\n", "args = dict(zip(SETTINGS_DEFINITIONS.keys(), values))\n", "\n", "# Overwrite specific settings\n", "args['algorithm'] = 'FCFL'\n", "args['dataset'] = 'FEMNIST'\n", "args['model'] = 'CNN'\n", "args['learning_rate'] = 0.1\n", "# args['n_clients'] = 100\n", "args['n_clients'] = 367\n", "# args['client_fraction'] = 1.0\n", "args['client_fraction'] = 0.2\n", "args['batch_size'] = 10\n", "args['n_epochs'] = 3\n", "args['use_gpu'] = True\n", "# args['partition_strategy'] = 'clustered'\n", "args['partition_strategy'] = 'non-iid'\n", "\n", "# One of target_accuracy or n_rounds MUST be set below\n", "args['target_accuracy'] = 0.80\n", "args['n_pre_cluster_rounds'] = 1\n", "args['n_rounds'] = 1\n", "\n", "args['cluster_dist_metric'] = 'manhattan'\n", "args['cluster_algorithm'] = 'hierarchical'\n", "args['cluster_hierarchical_linkage'] = 'average'\n", "# args['cluster_hierarchical_dist_threshold'] = 0.015\n", "args['cluster_hierarchical_dist_threshold'] = 1000.0\n", "\n", "args" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading FEMNIST dataset from cache..\n" ] } ], "source": [ "# Set up the experiment with the parsed arguments\n", "settings = ExperimentSettings(**args)\n", "# clients = [0,1,2,3,30,31,32,55,56,57] # subset of clients\n", "clients = None # all clients\n", "experiment = FCFLExperiment(settings, clients)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EXPERIMENT SETTINGS\n", "algorithm: FCFL\n", "batch_size: 10\n", "client_fraction: 0.2\n", "cluster_algorithm: hierarchical\n", "cluster_dist_metric: manhattan\n", "cluster_hierarchical_dist_threshold: 1000.0\n", "cluster_hierarchical_linkage: average\n", "dataset: FEMNIST\n", "empirical_risk_approximation_error_bound: None\n", "global_weight_delta_norm_threshold: None\n", "group_name: None\n", "id: 0\n", "job_dir: None\n", "learning_rate: 0.1\n", "max_client_weight_delta_norm_threshold: None\n", "model: CNN\n", "n_clients: 367\n", "n_clusters: 4\n", "n_epochs: 3\n", "n_pre_cluster_rounds: 1\n", "n_rounds: 1\n", "partition_strategy: non-iid\n", "seed: 0\n", "target_accuracy: 0.8\n", "use_gpu: True\n", "Using device: cpu\n", "\n", "Running FCFL with 367 client idxs: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366]\n", "Running learning for 1 communication rounds prior to clustering\n", "\n", "Communication round 1\n", "LOCAL TRAINING: Client #106, Epoch 1, #examples 162, Loss 4.025761085398057, Accuracy 0.04705882423064288\n", "LOCAL TRAINING: Client #106, Epoch 2, #examples 162, Loss 3.789903584648581, Accuracy 0.07058823634596433\n", "LOCAL TRAINING: Client #106, Epoch 3, #examples 162, Loss 3.5489099306218765, Accuracy 0.06470588331713396\n", "LOCAL TRAINING: Client #259, Epoch 1, #examples 281, Loss 4.001218590243109, Accuracy 0.02758620730761824\n", "LOCAL TRAINING: Client #259, Epoch 2, #examples 281, Loss 4.01915925946729, Accuracy 0.04137931096142736\n", "LOCAL TRAINING: Client #259, Epoch 3, #examples 281, Loss 3.8484830363043425, Accuracy 0.05862069052868876\n", "LOCAL TRAINING: Client #45, Epoch 1, #examples 317, Loss 3.939572051167488, Accuracy 0.058928572572767735\n", "LOCAL TRAINING: Client #45, Epoch 2, #examples 317, Loss 3.8000086173415184, Accuracy 0.10758928791619837\n", "LOCAL TRAINING: Client #45, Epoch 3, #examples 317, Loss 3.750340022146702, Accuracy 0.07633928721770644\n", "LOCAL TRAINING: Client #26, Epoch 1, #examples 228, Loss 4.008986151736716, Accuracy 0.03478260921395343\n", "LOCAL TRAINING: Client #26, Epoch 2, #examples 228, Loss 3.8562510220900825, Accuracy 0.06521739260010097\n", "LOCAL TRAINING: Client #26, Epoch 3, #examples 228, Loss 3.786247263783994, Accuracy 0.05543478319178457\n", "LOCAL TRAINING: Client #78, Epoch 1, #examples 156, Loss 4.056648567318916, Accuracy 0.05000000074505806\n", "LOCAL TRAINING: Client #78, Epoch 2, #examples 156, Loss 3.9039346128702164, Accuracy 0.06666666781529784\n", "LOCAL TRAINING: Client #78, Epoch 3, #examples 156, Loss 3.7251675873994827, Accuracy 0.08541666809469461\n", "LOCAL TRAINING: Client #347, Epoch 1, #examples 154, Loss 3.840477094054222, Accuracy 0.03125000046566129\n", "LOCAL TRAINING: Client #347, Epoch 2, #examples 154, Loss 3.7600158005952835, Accuracy 0.05625000083819032\n", "LOCAL TRAINING: Client #347, Epoch 3, #examples 154, Loss 3.607801243662834, Accuracy 0.05625000083819032\n", "LOCAL TRAINING: Client #90, Epoch 1, #examples 79, Loss 3.9516237676143646, Accuracy 0.05138888955116272\n", "LOCAL TRAINING: Client #90, Epoch 2, #examples 79, Loss 3.8673534989356995, Accuracy 0.07500000111758709\n", "LOCAL TRAINING: Client #90, Epoch 3, #examples 79, Loss 3.8745898008346558, Accuracy 0.10277777910232544\n", "LOCAL TRAINING: Client #311, Epoch 1, #examples 156, Loss 4.076413407921791, Accuracy 0.05625000083819032\n", "LOCAL TRAINING: Client #311, Epoch 2, #examples 156, Loss 3.8916871547698975, Accuracy 0.0750000006519258\n", "LOCAL TRAINING: Client #311, Epoch 3, #examples 156, Loss 3.753581792116165, Accuracy 0.08125000167638063\n", "LOCAL TRAINING: Client #239, Epoch 1, #examples 297, Loss 3.9399495840072634, Accuracy 0.06333333452542624\n", "LOCAL TRAINING: Client #239, Epoch 2, #examples 297, Loss 3.8145106474558514, Accuracy 0.040000000844399135\n", "LOCAL TRAINING: Client #239, Epoch 3, #examples 297, Loss 3.7849546988805134, Accuracy 0.060000001142422356\n", "LOCAL TRAINING: Client #6, Epoch 1, #examples 141, Loss 3.989412450790405, Accuracy 0.026666667064030966\n", "LOCAL TRAINING: Client #6, Epoch 2, #examples 141, Loss 4.348331960042318, Accuracy 0.04666666736205419\n", "LOCAL TRAINING: Client #6, Epoch 3, #examples 141, Loss 3.8689485867818196, Accuracy 0.07333333492279052\n", "LOCAL TRAINING: Client #120, Epoch 1, #examples 150, Loss 3.896051629384359, Accuracy 0.10000000248352686\n", "LOCAL TRAINING: Client #120, Epoch 2, #examples 150, Loss 3.9531935532887776, Accuracy 0.07333333442608515\n", "LOCAL TRAINING: Client #120, Epoch 3, #examples 150, Loss 3.8099211692810058, Accuracy 0.07333333492279052\n", "LOCAL TRAINING: Client #141, Epoch 1, #examples 162, Loss 4.0113292722141045, Accuracy 0.04705882466891233\n", "LOCAL TRAINING: Client #141, Epoch 2, #examples 162, Loss 3.814936034819659, Accuracy 0.0941176488995552\n", "LOCAL TRAINING: Client #141, Epoch 3, #examples 162, Loss 4.215255667181576, Accuracy 0.04117647120181252\n", "LOCAL TRAINING: Client #263, Epoch 1, #examples 311, Loss 3.9532028883695602, Accuracy 0.037500000558793545\n", "LOCAL TRAINING: Client #263, Epoch 2, #examples 311, Loss 3.8523600846529007, Accuracy 0.05312500079162419\n", "LOCAL TRAINING: Client #263, Epoch 3, #examples 311, Loss 3.8210351541638374, Accuracy 0.05312500079162419\n", "LOCAL TRAINING: Client #286, Epoch 1, #examples 149, Loss 3.9158090114593507, Accuracy 0.10000000149011612\n", "LOCAL TRAINING: Client #286, Epoch 2, #examples 149, Loss 4.022465419769287, Accuracy 0.16666667113701503\n", "LOCAL TRAINING: Client #286, Epoch 3, #examples 149, Loss 3.535874128341675, Accuracy 0.11407407621542613\n", "LOCAL TRAINING: Client #299, Epoch 1, #examples 351, Loss 3.9614780677689447, Accuracy 0.06388888984090751\n", "LOCAL TRAINING: Client #299, Epoch 2, #examples 351, Loss 3.920425640212165, Accuracy 0.05555555638339785\n", "LOCAL TRAINING: Client #299, Epoch 3, #examples 351, Loss 3.695358415444692, Accuracy 0.0777777795576387\n", "LOCAL TRAINING: Client #210, Epoch 1, #examples 341, Loss 3.979864127295358, Accuracy 0.0571428582072258\n", "LOCAL TRAINING: Client #210, Epoch 2, #examples 341, Loss 3.9884523051125664, Accuracy 0.06571428669350488\n", "LOCAL TRAINING: Client #210, Epoch 3, #examples 341, Loss 3.7138142449515206, Accuracy 0.07428571560553142\n", "LOCAL TRAINING: Client #294, Epoch 1, #examples 121, Loss 4.123697941119854, Accuracy 0.07692307864244168\n", "LOCAL TRAINING: Client #294, Epoch 2, #examples 121, Loss 4.110772059513972, Accuracy 0.03846153903466005\n", "LOCAL TRAINING: Client #294, Epoch 3, #examples 121, Loss 3.907003952906682, Accuracy 0.07692307864244168\n", "LOCAL TRAINING: Client #101, Epoch 1, #examples 160, Loss 3.981458008289337, Accuracy 0.05000000074505806\n", "LOCAL TRAINING: Client #101, Epoch 2, #examples 160, Loss 3.8018883019685745, Accuracy 0.06875000102445483\n", "LOCAL TRAINING: Client #101, Epoch 3, #examples 160, Loss 3.687374010682106, Accuracy 0.08750000176951289\n", "LOCAL TRAINING: Client #221, Epoch 1, #examples 165, Loss 3.9456401292015526, Accuracy 0.08235294240362503\n", "LOCAL TRAINING: Client #221, Epoch 2, #examples 165, Loss 3.7988407191108253, Accuracy 0.04117647120181252\n", "LOCAL TRAINING: Client #221, Epoch 3, #examples 165, Loss 3.5272611449746525, Accuracy 0.04705882423064288\n", "LOCAL TRAINING: Client #140, Epoch 1, #examples 153, Loss 4.030180439352989, Accuracy 0.05000000074505806\n", "LOCAL TRAINING: Client #140, Epoch 2, #examples 153, Loss 3.8354964554309845, Accuracy 0.018750000279396772\n", "LOCAL TRAINING: Client #140, Epoch 3, #examples 153, Loss 3.7879499047994614, Accuracy 0.05625000083819032\n", "LOCAL TRAINING: Client #142, Epoch 1, #examples 96, Loss 3.873860716819763, Accuracy 0.06666666865348816\n", "LOCAL TRAINING: Client #142, Epoch 2, #examples 96, Loss 4.117255210876465, Accuracy 0.08666666820645333\n", "LOCAL TRAINING: Client #142, Epoch 3, #examples 96, Loss 3.907428503036499, Accuracy 0.05000000149011612\n", "LOCAL TRAINING: Client #199, Epoch 1, #examples 118, Loss 4.082593818505605, Accuracy 0.08333333457509677\n", "LOCAL TRAINING: Client #199, Epoch 2, #examples 118, Loss 3.9553584853808084, Accuracy 0.06666666828095913\n", "LOCAL TRAINING: Client #199, Epoch 3, #examples 118, Loss 3.913279434045156, Accuracy 0.10208333532015483\n", "LOCAL TRAINING: Client #59, Epoch 1, #examples 148, Loss 4.051631848017375, Accuracy 0.05333333412806193\n", "LOCAL TRAINING: Client #59, Epoch 2, #examples 148, Loss 3.8673624992370605, Accuracy 0.06833333422740301\n", "LOCAL TRAINING: Client #59, Epoch 3, #examples 148, Loss 3.6706589221954347, Accuracy 0.04000000059604645\n", "LOCAL TRAINING: Client #293, Epoch 1, #examples 156, Loss 3.9232308715581894, Accuracy 0.08125000167638063\n", "LOCAL TRAINING: Client #293, Epoch 2, #examples 156, Loss 3.9663434624671936, Accuracy 0.07500000111758709\n", "LOCAL TRAINING: Client #293, Epoch 3, #examples 156, Loss 3.7470480501651764, Accuracy 0.08125000074505806\n", "LOCAL TRAINING: Client #302, Epoch 1, #examples 340, Loss 4.129856972133412, Accuracy 0.06176470746012295\n", "LOCAL TRAINING: Client #302, Epoch 2, #examples 340, Loss 3.959783483954037, Accuracy 0.058823530507438326\n", "LOCAL TRAINING: Client #302, Epoch 3, #examples 340, Loss 3.783940455492805, Accuracy 0.06764705983154914\n", "LOCAL TRAINING: Client #254, Epoch 1, #examples 356, Loss 4.000776807467143, Accuracy 0.044444445313678846\n", "LOCAL TRAINING: Client #254, Epoch 2, #examples 356, Loss 3.8463998238245645, Accuracy 0.07129629742768076\n", "LOCAL TRAINING: Client #254, Epoch 3, #examples 356, Loss 3.753680878215366, Accuracy 0.08611111239426666\n", "LOCAL TRAINING: Client #68, Epoch 1, #examples 267, Loss 4.049066914452447, Accuracy 0.033333333830038704\n", "LOCAL TRAINING: Client #68, Epoch 2, #examples 267, Loss 3.80499169561598, Accuracy 0.059259260418238466\n", "LOCAL TRAINING: Client #68, Epoch 3, #examples 267, Loss 3.9143542360376427, Accuracy 0.04814814941750632\n", "LOCAL TRAINING: Client #332, Epoch 1, #examples 141, Loss 4.14985187848409, Accuracy 0.04666666736205419\n", "LOCAL TRAINING: Client #332, Epoch 2, #examples 141, Loss 3.97607208887736, Accuracy 0.09333333472410837\n", "LOCAL TRAINING: Client #332, Epoch 3, #examples 141, Loss 3.7641613483428955, Accuracy 0.10666666726271311\n", "LOCAL TRAINING: Client #113, Epoch 1, #examples 154, Loss 3.903986230492592, Accuracy 0.07812500093132257\n", "LOCAL TRAINING: Client #113, Epoch 2, #examples 154, Loss 3.897710844874382, Accuracy 0.06875000149011612\n", "LOCAL TRAINING: Client #113, Epoch 3, #examples 154, Loss 3.5743749290704727, Accuracy 0.05000000074505806\n", "LOCAL TRAINING: Client #191, Epoch 1, #examples 92, Loss 3.8925856828689573, Accuracy 0.030000000447034835\n", "LOCAL TRAINING: Client #191, Epoch 2, #examples 92, Loss 3.562303304672241, Accuracy 0.09000000208616257\n", "LOCAL TRAINING: Client #191, Epoch 3, #examples 92, Loss 3.982373762130737, Accuracy 0.06000000089406967\n", "LOCAL TRAINING: Client #124, Epoch 1, #examples 153, Loss 3.9473234862089157, Accuracy 0.05625000083819032\n", "LOCAL TRAINING: Client #124, Epoch 2, #examples 153, Loss 3.7593023777008057, Accuracy 0.09791666874662042\n", "LOCAL TRAINING: Client #124, Epoch 3, #examples 153, Loss 3.9751392751932144, Accuracy 0.07708333525806665\n", "LOCAL TRAINING: Client #102, Epoch 1, #examples 151, Loss 4.030202269554138, Accuracy 0.0875000013038516\n", "LOCAL TRAINING: Client #102, Epoch 2, #examples 151, Loss 3.8981972336769104, Accuracy 0.10625000158324838\n", "LOCAL TRAINING: Client #102, Epoch 3, #examples 151, Loss 3.658835008740425, Accuracy 0.08125000121071935\n", "LOCAL TRAINING: Client #15, Epoch 1, #examples 101, Loss 4.097196860746904, Accuracy 0.06363636458461935\n", "LOCAL TRAINING: Client #15, Epoch 2, #examples 101, Loss 4.018899028951472, Accuracy 0.14545454694466156\n", "LOCAL TRAINING: Client #15, Epoch 3, #examples 101, Loss 3.9318913329731333, Accuracy 0.07272727381099355\n", "LOCAL TRAINING: Client #37, Epoch 1, #examples 246, Loss 4.031639423370361, Accuracy 0.028000000417232513\n", "LOCAL TRAINING: Client #37, Epoch 2, #examples 246, Loss 3.8902157592773436, Accuracy 0.06000000089406967\n", "LOCAL TRAINING: Client #37, Epoch 3, #examples 246, Loss 3.7189461994171142, Accuracy 0.06000000089406967\n", "LOCAL TRAINING: Client #54, Epoch 1, #examples 156, Loss 4.029694646596909, Accuracy 0.037500000558793545\n", "LOCAL TRAINING: Client #54, Epoch 2, #examples 156, Loss 3.766818955540657, Accuracy 0.09375000139698386\n", "LOCAL TRAINING: Client #54, Epoch 3, #examples 156, Loss 3.683678761124611, Accuracy 0.06875000102445483\n", "LOCAL TRAINING: Client #5, Epoch 1, #examples 124, Loss 3.9644967959477353, Accuracy 0.03846153903466005\n", "LOCAL TRAINING: Client #5, Epoch 2, #examples 124, Loss 4.122538970066951, Accuracy 0.06923077026238808\n", "LOCAL TRAINING: Client #5, Epoch 3, #examples 124, Loss 4.033237915772658, Accuracy 0.09615384787321091\n", "LOCAL TRAINING: Client #60, Epoch 1, #examples 149, Loss 3.9309019724527996, Accuracy 0.0800000011920929\n", "LOCAL TRAINING: Client #60, Epoch 2, #examples 149, Loss 3.761277707417806, Accuracy 0.06740740835666656\n", "LOCAL TRAINING: Client #60, Epoch 3, #examples 149, Loss 3.6413535118103026, Accuracy 0.06666666815678278\n", "LOCAL TRAINING: Client #132, Epoch 1, #examples 236, Loss 3.997826894124349, Accuracy 0.037500000558793545\n", "LOCAL TRAINING: Client #132, Epoch 2, #examples 236, Loss 3.926369865735372, Accuracy 0.04583333401630322\n", "LOCAL TRAINING: Client #132, Epoch 3, #examples 236, Loss 3.8563255965709686, Accuracy 0.033333333830038704\n", "LOCAL TRAINING: Client #264, Epoch 1, #examples 291, Loss 3.9920101960500083, Accuracy 0.026666667064030966\n", "LOCAL TRAINING: Client #264, Epoch 2, #examples 291, Loss 3.8356170018513995, Accuracy 0.016666666915019352\n", "LOCAL TRAINING: Client #264, Epoch 3, #examples 291, Loss 3.9639116684595743, Accuracy 0.026666667064030966\n", "LOCAL TRAINING: Client #64, Epoch 1, #examples 144, Loss 4.102751795450846, Accuracy 0.04333333373069763\n", "LOCAL TRAINING: Client #64, Epoch 2, #examples 144, Loss 3.990520413716634, Accuracy 0.03333333432674408\n", "LOCAL TRAINING: Client #64, Epoch 3, #examples 144, Loss 3.8880707263946532, Accuracy 0.04000000059604645\n", "LOCAL TRAINING: Client #206, Epoch 1, #examples 265, Loss 4.031802195089835, Accuracy 0.06296296390118422\n", "LOCAL TRAINING: Client #206, Epoch 2, #examples 265, Loss 3.95031060995879, Accuracy 0.029629630071145517\n", "LOCAL TRAINING: Client #206, Epoch 3, #examples 265, Loss 3.7979244832639343, Accuracy 0.04444444510671827\n", "LOCAL TRAINING: Client #303, Epoch 1, #examples 303, Loss 3.96630185650241, Accuracy 0.041935484495855144\n", "LOCAL TRAINING: Client #303, Epoch 2, #examples 303, Loss 3.8984146733437814, Accuracy 0.041935484495855144\n", "LOCAL TRAINING: Client #303, Epoch 3, #examples 303, Loss 3.7993769107326383, Accuracy 0.058064516994260976\n", "LOCAL TRAINING: Client #212, Epoch 1, #examples 249, Loss 3.9946588134765624, Accuracy 0.02400000035762787\n", "LOCAL TRAINING: Client #212, Epoch 2, #examples 249, Loss 3.8569357776641846, Accuracy 0.04000000059604645\n", "LOCAL TRAINING: Client #212, Epoch 3, #examples 249, Loss 3.782520303726196, Accuracy 0.03644444495439529\n", "LOCAL TRAINING: Client #260, Epoch 1, #examples 321, Loss 4.0048651261763135, Accuracy 0.048484849207329025\n", "LOCAL TRAINING: Client #260, Epoch 2, #examples 321, Loss 4.110493963414973, Accuracy 0.039393939980954834\n", "LOCAL TRAINING: Client #260, Epoch 3, #examples 321, Loss 3.9007642124638413, Accuracy 0.048484849433104195\n", "LOCAL TRAINING: Client #20, Epoch 1, #examples 143, Loss 3.99950483640035, Accuracy 0.06000000089406967\n", "LOCAL TRAINING: Client #20, Epoch 2, #examples 143, Loss 3.9251832167307534, Accuracy 0.10222222457329432\n", "LOCAL TRAINING: Client #20, Epoch 3, #examples 143, Loss 3.659610652923584, Accuracy 0.10444444666306178\n", "LOCAL TRAINING: Client #305, Epoch 1, #examples 315, Loss 4.076060399413109, Accuracy 0.06250000139698386\n", "LOCAL TRAINING: Client #305, Epoch 2, #examples 315, Loss 3.7997320368885994, Accuracy 0.07812500139698386\n", "LOCAL TRAINING: Client #305, Epoch 3, #examples 315, Loss 3.6679616048932076, Accuracy 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Loss 3.9173221349716187, Accuracy 0.016666666915019352\n", "LOCAL TRAINING: Client #74, Epoch 1, #examples 160, Loss 4.0586941838264465, Accuracy 0.05000000074505806\n", "LOCAL TRAINING: Client #74, Epoch 2, #examples 160, Loss 3.9088023900985718, Accuracy 0.05625000083819032\n", "LOCAL TRAINING: Client #74, Epoch 3, #examples 160, Loss 3.8079294860363007, Accuracy 0.0750000006519258\n", "LOCAL TRAINING: Client #170, Epoch 1, #examples 304, Loss 4.043207537743353, Accuracy 0.054838710494579807\n", "LOCAL TRAINING: Client #170, Epoch 2, #examples 304, Loss 3.9121744017447195, Accuracy 0.06935484010365701\n", "LOCAL TRAINING: Client #170, Epoch 3, #examples 304, Loss 3.726089762103173, Accuracy 0.07096774299298564\n", "LOCAL TRAINING: Client #114, Epoch 1, #examples 133, Loss 4.037273798670087, Accuracy 0.05000000074505806\n", "LOCAL TRAINING: Client #114, Epoch 2, #examples 133, Loss 4.029895527022226, Accuracy 0.04285714349576405\n", "LOCAL TRAINING: Client #114, Epoch 3, #examples 133, Loss 3.8530133281435286, Accuracy 0.04523809626698494\n", "LOCAL TRAINING: Client #295, Epoch 1, #examples 168, Loss 4.093807108262006, Accuracy 0.04705882466891233\n", "LOCAL TRAINING: Client #295, Epoch 2, #examples 168, Loss 3.9462636078105255, Accuracy 0.07941176567007513\n", "LOCAL TRAINING: Client #295, Epoch 3, #examples 168, Loss 3.68862478873309, Accuracy 0.06617647190304364\n", "CLUSTER: 1, MEAN TEST ACCURACY: 0.09158795950705902, 10th percentile: 0.0, 90th percentile: 0.20000000298023224, % clients reaching target accuracy: 0.0\n", "GLOBAL WEIGHTS DELTA NORM: 1.2260942459106445\n", "58s elapsed\n", "Training complete in 1 communication rounds\n", "\n", "Running 1 round of training on all client in cluster to obtain client weight updates\n", "LOCAL TRAINING: Client #0, Epoch 1, #examples 136, Loss 3.719107593808855, Accuracy 0.07619047750319753\n", "LOCAL TRAINING: Client #0, Epoch 2, #examples 136, Loss 3.4431884799684798, Accuracy 0.11428571758525712\n", "LOCAL TRAINING: Client #0, Epoch 3, #examples 136, Loss 3.311733058520726, Accuracy 0.11428571598870414\n", "LOCAL TRAINING: Client #1, Epoch 1, #examples 134, Loss 3.622457895960127, Accuracy 0.10714285927159446\n", "LOCAL TRAINING: Client #1, Epoch 2, #examples 134, Loss 3.3381348507744923, Accuracy 0.14642857387661934\n", "LOCAL TRAINING: Client #1, Epoch 3, #examples 134, Loss 2.957041246550424, Accuracy 0.2392857170530728\n", "LOCAL TRAINING: Client #2, Epoch 1, #examples 153, Loss 3.521236404776573, Accuracy 0.06250000093132257\n", "LOCAL TRAINING: Client #2, Epoch 2, #examples 153, Loss 3.4076443165540695, Accuracy 0.037500000558793545\n", "LOCAL TRAINING: Client #2, Epoch 3, #examples 153, Loss 3.15698903799057, Accuracy 0.08750000176951289\n", "LOCAL TRAINING: Client #3, Epoch 1, #examples 154, Loss 3.761137768626213, Accuracy 0.06875000102445483\n", "LOCAL TRAINING: Client #3, Epoch 2, #examples 154, Loss 3.5343723595142365, Accuracy 0.056250001303851604\n", "LOCAL TRAINING: Client 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Loss 3.2240670007817886, Accuracy 0.18823529648430207\n", "LOCAL TRAINING: Client #10, Epoch 1, #examples 57, Loss 3.8468616803487143, Accuracy 0.05000000074505806\n", "LOCAL TRAINING: Client #10, Epoch 2, #examples 57, Loss 3.6582193771998086, Accuracy 0.033333333830038704\n", "LOCAL TRAINING: Client #10, Epoch 3, #examples 57, Loss 3.7055156230926514, Accuracy 0.06666666766007741\n", "LOCAL TRAINING: Client #11, Epoch 1, #examples 90, Loss 3.88139361805386, Accuracy 0.12222222404347526\n", "LOCAL TRAINING: Client #11, Epoch 2, #examples 90, Loss 3.769010649787055, Accuracy 0.1111111127667957\n", "LOCAL TRAINING: Client #11, Epoch 3, #examples 90, Loss 3.6961136129167347, Accuracy 0.1333333361479971\n", "LOCAL TRAINING: Client #12, Epoch 1, #examples 94, Loss 3.8783025979995727, Accuracy 0.060000001639127734\n", "LOCAL TRAINING: Client #12, Epoch 2, #examples 94, Loss 3.876572322845459, Accuracy 0.010000000149011612\n", "LOCAL TRAINING: Client #12, Epoch 3, #examples 94, Loss 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Loss 3.5404962062835694, Accuracy 0.07333333492279052\n", "LOCAL TRAINING: Client #362, Epoch 3, #examples 144, Loss 3.31372062365214, Accuracy 0.06666666815678278\n", "LOCAL TRAINING: Client #363, Epoch 1, #examples 154, Loss 3.5752921402454376, Accuracy 0.1000000019557774\n", "LOCAL TRAINING: Client #363, Epoch 2, #examples 154, Loss 3.425969824194908, Accuracy 0.05625000083819032\n", "LOCAL TRAINING: Client #363, Epoch 3, #examples 154, Loss 3.176957607269287, Accuracy 0.18750000419095159\n", "LOCAL TRAINING: Client #364, Epoch 1, #examples 144, Loss 3.6357102235158285, Accuracy 0.10333333512147268\n", "LOCAL TRAINING: Client #364, Epoch 2, #examples 144, Loss 3.4074779828389485, Accuracy 0.04666666736205419\n", "LOCAL TRAINING: Client #364, Epoch 3, #examples 144, Loss 3.1871330738067627, Accuracy 0.10000000149011612\n", "LOCAL TRAINING: Client #365, Epoch 1, #examples 144, Loss 3.5518174807230634, Accuracy 0.05333333412806193\n", "LOCAL TRAINING: Client #365, Epoch 2, #examples 144, Loss 3.4767707506815593, Accuracy 0.08333333532015483\n", "LOCAL TRAINING: Client #365, Epoch 3, #examples 144, Loss 3.2923162460327147, Accuracy 0.09666666835546493\n", "LOCAL TRAINING: Client #366, Epoch 1, #examples 134, Loss 3.483571478298732, Accuracy 0.10357142984867096\n", "LOCAL TRAINING: Client #366, Epoch 2, #examples 134, Loss 3.299341951097761, Accuracy 0.07857143080660275\n", "LOCAL TRAINING: Client #366, Epoch 3, #examples 134, Loss 3.223787103380476, Accuracy 0.07857142974223409\n", "[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 5 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n", "6\n", "6 clusters found [[51, 301], [187, 335], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366], [136], [29], [300]]\n", "\n", "Running FCFL with 2 client idxs: [51, 301]\n", "Running learning for 1 communication rounds\n", "\n", "Communication round 1\n", "LOCAL TRAINING: Client #301, Epoch 1, #examples 349, Loss 3.7860889775412425, Accuracy 0.02920634959425245\n", "LOCAL TRAINING: Client #301, Epoch 2, #examples 349, Loss 3.5962531770978656, Accuracy 0.06285714400666101\n", "LOCAL TRAINING: Client #301, Epoch 3, #examples 349, Loss 3.2266553401947022, Accuracy 0.1546031783734049\n", "CLUSTER: 1.1, MEAN TEST ACCURACY: 0.15311652794480324, 10th percentile: 0.11951219514012337, 90th percentile: 0.18672086074948313, % clients reaching target accuracy: 0.0\n", "GLOBAL WEIGHTS DELTA NORM: 2.76242995262146\n", "355s elapsed\n", "Training complete in 1 communication rounds\n", "\n", "Running 1 round of training on all client in cluster to obtain client weight updates\n", "LOCAL TRAINING: Client #51, Epoch 1, #examples 357, Loss 2.8837394648128085, Accuracy 0.20515873490108383\n", "LOCAL TRAINING: Client #51, Epoch 2, #examples 357, Loss 2.1606558528211384, Accuracy 0.38015873461133903\n", "LOCAL TRAINING: Client #51, Epoch 3, #examples 357, Loss 1.6433781998025045, Accuracy 0.5503968331548903\n", "LOCAL TRAINING: Client #301, Epoch 1, #examples 349, Loss 2.5067322322300503, Accuracy 0.329523814363139\n", "LOCAL TRAINING: Client #301, Epoch 2, #examples 349, Loss 1.9113528319767543, Accuracy 0.46380953171423506\n", "LOCAL TRAINING: Client #301, Epoch 3, #examples 349, Loss 1.322365437235151, Accuracy 0.6193650837455477\n", "[1 0]\n", "2\n", "Cluster 1.1 will not be split any further\n", "\n", "Running FCFL with 2 client idxs: [187, 335]\n", "Running learning for 1 communication rounds\n", "\n", "Communication round 1\n", "LOCAL TRAINING: Client #187, Epoch 1, #examples 121, Loss 3.3400486432589016, Accuracy 0.21538461859409624\n", "LOCAL TRAINING: Client #187, Epoch 2, #examples 121, Loss 3.748322633596567, Accuracy 0.17692307841319305\n", "LOCAL TRAINING: Client #187, Epoch 3, #examples 121, Loss 2.9620645413031945, Accuracy 0.31538461779172605\n", "CLUSTER: 1.2, MEAN TEST ACCURACY: 0.0555555559694767, 10th percentile: 0.01111111119389534, 90th percentile: 0.10000000074505806, % clients reaching target accuracy: 0.0\n", "GLOBAL WEIGHTS DELTA NORM: 2.1286003589630127\n", "358s elapsed\n", "Training complete in 1 communication rounds\n", "\n", "Running 1 round of training on all client in cluster to obtain client weight updates\n", "LOCAL TRAINING: Client #187, Epoch 1, #examples 121, Loss 3.1511778097886305, Accuracy 0.21538461859409624\n", "LOCAL TRAINING: Client #187, Epoch 2, #examples 121, Loss 2.5232563752394457, Accuracy 0.32307692961050916\n", "LOCAL TRAINING: Client #187, Epoch 3, #examples 121, Loss 2.8770133898808408, Accuracy 0.2769230821957955\n", "LOCAL TRAINING: Client #335, Epoch 1, #examples 132, Loss 3.407773188182286, Accuracy 0.10714285980377879\n", "LOCAL TRAINING: Client #335, Epoch 2, #examples 132, Loss 2.6889039278030396, Accuracy 0.17857143176453455\n", "LOCAL TRAINING: Client #335, Epoch 3, #examples 132, Loss 2.674444692475455, Accuracy 0.2785714359155723\n", "[0 0]\n", "1\n", "Cluster 1.2 will not be split any further\n", "\n", "Running FCFL with 360 client idxs: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366]\n", "Running learning for 1 communication rounds\n", "\n", "Communication round 1\n", "LOCAL TRAINING: Client #295, Epoch 1, #examples 168, Loss 3.3383127100327434, Accuracy 0.18676470921320074\n", "LOCAL TRAINING: Client #295, Epoch 2, #examples 168, Loss 2.678895817083471, Accuracy 0.3500000067493495\n", "LOCAL TRAINING: Client #295, Epoch 3, #examples 168, Loss 2.3228037497576546, Accuracy 0.4838235351969214\n", "LOCAL TRAINING: Client #238, Epoch 1, #examples 262, Loss 3.3818288290942156, Accuracy 0.18148148501360858\n", "LOCAL TRAINING: Client #238, Epoch 2, #examples 262, Loss 2.7299194909908153, Accuracy 0.3111111179546074\n", "LOCAL TRAINING: Client #238, Epoch 3, #examples 262, Loss 2.292710776682253, Accuracy 0.3629629678196377\n", "LOCAL TRAINING: Client #41, Epoch 1, #examples 235, Loss 3.310310512781143, Accuracy 0.1625000030423204\n", "LOCAL TRAINING: Client #41, Epoch 2, #examples 235, Loss 2.772182270884514, Accuracy 0.3000000047807892\n", "LOCAL TRAINING: Client #41, Epoch 3, #examples 235, Loss 2.2992302030324936, Accuracy 0.4458333409080903\n", "LOCAL TRAINING: Client #16, Epoch 1, #examples 138, Loss 3.3811738320759366, Accuracy 0.14285714711461747\n", "LOCAL TRAINING: Client #16, Epoch 2, #examples 138, Loss 2.8248225109917775, Accuracy 0.208928576537541\n", "LOCAL TRAINING: Client #16, Epoch 3, #examples 138, Loss 2.4656414900507246, Accuracy 0.391071434531893\n", "LOCAL TRAINING: Client #14, Epoch 1, #examples 126, Loss 3.3526476529928355, Accuracy 0.3051282110122534\n", "LOCAL TRAINING: Client #14, Epoch 2, #examples 126, Loss 2.635748111284696, Accuracy 0.4051282130754911\n", "LOCAL TRAINING: Client #14, Epoch 3, #examples 126, Loss 2.405513800107516, Accuracy 0.4692307704916367\n", "LOCAL TRAINING: Client #160, Epoch 1, #examples 299, Loss 3.204859177271525, Accuracy 0.1974074107905229\n", "LOCAL TRAINING: Client #160, Epoch 2, #examples 299, Loss 2.4484858592351277, Accuracy 0.34407407939434054\n", "LOCAL TRAINING: Client #160, Epoch 3, #examples 299, Loss 1.992249172925949, Accuracy 0.4059259320298831\n", "LOCAL TRAINING: Client #270, Epoch 1, #examples 310, Loss 3.5813952645947857, Accuracy 0.11612903494988719\n", "LOCAL TRAINING: Client #270, Epoch 2, #examples 310, Loss 3.0806811394230014, Accuracy 0.18709677794287283\n", "LOCAL TRAINING: Client #270, Epoch 3, #examples 310, Loss 2.6466259033449235, Accuracy 0.28387097437535563\n", "LOCAL TRAINING: Client #32, Epoch 1, #examples 112, Loss 3.237121264139811, Accuracy 0.23333333556850752\n", "LOCAL TRAINING: Client #32, Epoch 2, #examples 112, Loss 2.6365256905555725, Accuracy 0.35833333618938923\n", "LOCAL TRAINING: Client #32, Epoch 3, #examples 112, Loss 2.3033737341562905, Accuracy 0.42500000695387524\n", "LOCAL TRAINING: Client #255, Epoch 1, #examples 342, Loss 3.666421570096697, Accuracy 0.10285714481558118\n", "LOCAL TRAINING: Client #255, Epoch 2, #examples 342, Loss 2.984183502197266, Accuracy 0.22285714724234174\n", "LOCAL TRAINING: Client #255, Epoch 3, #examples 342, Loss 2.571177656309945, Accuracy 0.32000000604561396\n", "LOCAL TRAINING: Client #105, Epoch 1, #examples 153, Loss 3.7714435160160065, Accuracy 0.09583333553746343\n", "LOCAL TRAINING: Client #105, Epoch 2, #examples 153, Loss 3.0387419164180756, Accuracy 0.20833333814516664\n", "LOCAL TRAINING: Client #105, Epoch 3, #examples 153, Loss 2.6625100150704384, Accuracy 0.36041667172685266\n", "LOCAL TRAINING: Client #101, Epoch 1, #examples 160, Loss 3.201066493988037, Accuracy 0.2187500037252903\n", "LOCAL TRAINING: Client #101, Epoch 2, #examples 160, Loss 2.5144233033061028, Accuracy 0.4187500076368451\n", "LOCAL TRAINING: Client #101, Epoch 3, #examples 160, Loss 2.1207121163606644, Accuracy 0.5500000026077032\n", "LOCAL TRAINING: Client #281, Epoch 1, #examples 147, Loss 3.3192911465962727, Accuracy 0.21238095611333846\n", "LOCAL TRAINING: Client #281, Epoch 2, #examples 147, Loss 2.8612932920455934, Accuracy 0.3019047682483991\n", "LOCAL TRAINING: Client #281, Epoch 3, #examples 147, Loss 2.478187847137451, Accuracy 0.3314285765091578\n", "LOCAL TRAINING: Client #219, Epoch 1, #examples 164, Loss 3.3462312782511994, Accuracy 0.13529412010136774\n", "LOCAL TRAINING: Client #219, Epoch 2, #examples 164, Loss 2.9178909694447235, Accuracy 0.2500000035061556\n", "LOCAL TRAINING: Client #219, Epoch 3, #examples 164, Loss 2.5958430135951325, Accuracy 0.3823529468739734\n", "LOCAL TRAINING: Client #318, Epoch 1, #examples 322, Loss 3.48088398846713, Accuracy 0.1545454575256868\n", "LOCAL TRAINING: Client #318, Epoch 2, #examples 322, Loss 2.8198320070902505, Accuracy 0.2575757632201368\n", "LOCAL TRAINING: Client #318, Epoch 3, #examples 322, Loss 2.3800134911681665, Accuracy 0.3181818238261974\n", "LOCAL TRAINING: Client #87, Epoch 1, #examples 117, Loss 3.6409838994344077, Accuracy 0.1333333346992731\n", "LOCAL TRAINING: Client #87, Epoch 2, #examples 117, Loss 2.947840392589569, Accuracy 0.24166667150954405\n", "LOCAL TRAINING: Client #87, Epoch 3, #examples 117, Loss 2.6656753520170846, Accuracy 0.3488095309585333\n", "LOCAL TRAINING: Client #358, Epoch 1, #examples 142, Loss 3.1257485230763753, Accuracy 0.30000000447034836\n", "LOCAL TRAINING: Client #358, Epoch 2, #examples 142, Loss 2.8116781949996947, Accuracy 0.34000000556310017\n", "LOCAL TRAINING: Client #358, Epoch 3, #examples 142, Loss 2.3960564851760866, Accuracy 0.48666667342185976\n", "LOCAL TRAINING: Client #42, Epoch 1, #examples 291, Loss 3.805022048950195, Accuracy 0.08000000193715096\n", "LOCAL TRAINING: Client #42, Epoch 2, #examples 291, Loss 3.16641681989034, Accuracy 0.1966666708389918\n", "LOCAL TRAINING: Client #42, Epoch 3, #examples 291, Loss 2.963389277458191, Accuracy 0.21333333775401114\n", "LOCAL TRAINING: Client #150, Epoch 1, #examples 139, Loss 3.5783584799085344, Accuracy 0.14285714605024882\n", "LOCAL TRAINING: Client #150, Epoch 2, #examples 139, Loss 3.028821281024388, Accuracy 0.25952381427798954\n", "LOCAL TRAINING: Client #150, Epoch 3, #examples 139, Loss 2.726616850921086, Accuracy 0.3333333395421505\n", "LOCAL TRAINING: Client #11, Epoch 1, #examples 90, Loss 3.7529575294918485, Accuracy 0.13333333532015482\n", "LOCAL TRAINING: Client #11, Epoch 2, #examples 90, Loss 3.219204584757487, Accuracy 0.25555556101931465\n", "LOCAL TRAINING: Client #11, Epoch 3, #examples 90, Loss 2.9976723194122314, Accuracy 0.3888888971673118\n", "LOCAL TRAINING: Client #37, Epoch 1, #examples 246, Loss 3.3110922622680663, Accuracy 0.19866667032241822\n", "LOCAL TRAINING: Client #37, Epoch 2, #examples 246, Loss 2.753968105316162, Accuracy 0.33333333969116213\n", "LOCAL TRAINING: Client #37, Epoch 3, #examples 246, Loss 2.3069966936111452, Accuracy 0.4213333386182785\n", "LOCAL TRAINING: Client #347, Epoch 1, #examples 154, Loss 3.392119973897934, Accuracy 0.1593750030733645\n", "LOCAL TRAINING: Client #347, Epoch 2, #examples 154, Loss 2.742537148296833, Accuracy 0.2843750035390258\n", "LOCAL TRAINING: Client #347, Epoch 3, #examples 154, Loss 2.4611948430538177, Accuracy 0.500000003259629\n", "LOCAL TRAINING: Client #350, Epoch 1, #examples 158, Loss 3.795993000268936, Accuracy 0.08125000167638063\n", "LOCAL TRAINING: Client #350, Epoch 2, #examples 158, Loss 3.3152306377887726, Accuracy 0.12187500298023224\n", "LOCAL TRAINING: Client #350, Epoch 3, #examples 158, Loss 3.055229440331459, Accuracy 0.21093750419095159\n", "LOCAL TRAINING: Client #349, Epoch 1, #examples 160, Loss 3.1290673166513443, Accuracy 0.23750000400468707\n", "LOCAL TRAINING: Client #349, Epoch 2, #examples 160, Loss 2.551916182041168, Accuracy 0.381250009406358\n", "LOCAL TRAINING: Client #349, Epoch 3, #examples 160, Loss 2.230291470885277, Accuracy 0.4375000079162419\n", "LOCAL TRAINING: Client #97, Epoch 1, #examples 162, Loss 3.1056997355292824, Accuracy 0.3235294192152865\n", "LOCAL TRAINING: Client #97, Epoch 2, #examples 162, Loss 2.757891423561994, Accuracy 0.38823530077934265\n", "LOCAL TRAINING: Client #97, Epoch 3, #examples 162, Loss 1.9807095562710482, Accuracy 0.5647058859467506\n", "LOCAL TRAINING: Client #119, Epoch 1, #examples 162, Loss 3.453105702119715, Accuracy 0.16470588524551952\n", "LOCAL TRAINING: Client #119, Epoch 2, #examples 162, Loss 2.9651335758321427, Accuracy 0.2764705941081047\n", "LOCAL TRAINING: Client #119, Epoch 3, #examples 162, Loss 2.532372278325698, Accuracy 0.35882353651172977\n", "LOCAL TRAINING: Client #341, Epoch 1, #examples 133, Loss 3.636105145726885, Accuracy 0.09285714477300644\n", "LOCAL TRAINING: Client #341, Epoch 2, #examples 133, Loss 2.8144521713256836, Accuracy 0.3333333432674408\n", "LOCAL TRAINING: Client #341, Epoch 3, #examples 133, Loss 2.247451526778085, Accuracy 0.4976190562759127\n", "LOCAL TRAINING: Client #5, Epoch 1, #examples 124, Loss 3.739699308688824, Accuracy 0.11538461653085855\n", "LOCAL TRAINING: Client #5, Epoch 2, #examples 124, Loss 3.087801144673274, Accuracy 0.2269230828835414\n", "LOCAL TRAINING: Client #5, Epoch 3, #examples 124, Loss 2.8984580773573656, Accuracy 0.2576923123919047\n", "LOCAL TRAINING: Client #69, Epoch 1, #examples 324, Loss 3.0364528569308193, Accuracy 0.24545455091830456\n", "LOCAL TRAINING: Client #69, Epoch 2, #examples 324, Loss 2.2853253068345967, Accuracy 0.3954545532663663\n", "LOCAL TRAINING: Client #69, Epoch 3, #examples 324, Loss 1.712557408845786, Accuracy 0.5409090943408735\n", "LOCAL TRAINING: Client #23, Epoch 1, #examples 272, Loss 3.6217258146830966, Accuracy 0.12500000266092165\n", "LOCAL TRAINING: Client #23, Epoch 2, #examples 272, Loss 2.921415533338274, Accuracy 0.2535714355430433\n", "LOCAL TRAINING: Client #23, Epoch 3, #examples 272, Loss 2.6333886682987213, Accuracy 0.3178571497223207\n", "LOCAL TRAINING: Client #1, Epoch 1, #examples 134, Loss 3.052861145564488, Accuracy 0.23571429029107094\n", "LOCAL TRAINING: Client #1, Epoch 2, #examples 134, Loss 2.079824847834451, Accuracy 0.4607142944421087\n", "LOCAL TRAINING: Client #1, Epoch 3, #examples 134, Loss 1.9031761033194405, Accuracy 0.5321428605488369\n", "LOCAL TRAINING: Client #353, Epoch 1, #examples 157, Loss 3.4962088316679, Accuracy 0.15000000409781933\n", "LOCAL TRAINING: Client #353, Epoch 2, #examples 157, Loss 2.9817205369472504, Accuracy 0.2803571498952806\n", "LOCAL TRAINING: Client #353, Epoch 3, #examples 157, Loss 2.6375655233860016, Accuracy 0.37232143757864833\n", "LOCAL TRAINING: Client #264, Epoch 1, #examples 291, Loss 3.248466420173645, Accuracy 0.17666667128602664\n", "LOCAL TRAINING: Client #264, Epoch 2, #examples 291, Loss 2.6186529795328775, Accuracy 0.31000000685453416\n", "LOCAL TRAINING: Client #264, Epoch 3, #examples 291, Loss 4.153943196932475, Accuracy 0.033333333830038704\n", "LOCAL TRAINING: Client #224, Epoch 1, #examples 161, Loss 3.0639534697813144, Accuracy 0.25294118199278326\n", "LOCAL TRAINING: Client #224, Epoch 2, #examples 161, Loss 2.6872775975395653, Accuracy 0.3176470644333783\n", "LOCAL TRAINING: Client #224, Epoch 3, #examples 161, Loss 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Accuracy 0.3117647087749313\n", "LOCAL TRAINING: Client #326, Epoch 1, #examples 162, Loss 3.2924959028468415, Accuracy 0.1823529443320106\n", "LOCAL TRAINING: Client #326, Epoch 2, #examples 162, Loss 2.4700547036002662, Accuracy 0.4470588284380296\n", "LOCAL TRAINING: Client #326, Epoch 3, #examples 162, Loss 2.1327664431403663, Accuracy 0.5588235399302315\n", "LOCAL TRAINING: Client #61, Epoch 1, #examples 163, Loss 3.5012015735401825, Accuracy 0.17254902334774241\n", "LOCAL TRAINING: Client #61, Epoch 2, #examples 163, Loss 2.9021745990304386, Accuracy 0.25686275038649053\n", "LOCAL TRAINING: Client #61, Epoch 3, #examples 163, Loss 2.5038505231632904, Accuracy 0.386274516582489\n", "LOCAL TRAINING: Client #271, Epoch 1, #examples 363, Loss 3.3428501567325077, Accuracy 0.1765765807515866\n", "LOCAL TRAINING: Client #271, Epoch 2, #examples 363, Loss 2.704302146628096, Accuracy 0.33333334064966924\n", "LOCAL TRAINING: Client #271, Epoch 3, #examples 363, Loss 2.0062723900820756, Accuracy 0.4639639707433211\n", "LOCAL TRAINING: Client #254, Epoch 1, #examples 356, Loss 3.3703806400299072, Accuracy 0.19351852416164345\n", "LOCAL TRAINING: Client #254, Epoch 2, #examples 356, Loss 2.5834026535352073, Accuracy 0.3407407458871603\n", "LOCAL TRAINING: Client #254, Epoch 3, #examples 356, Loss 2.11912240087986, Accuracy 0.417592600815826\n", "LOCAL TRAINING: Client #317, Epoch 1, #examples 352, Loss 3.5516447093751697, Accuracy 0.11388889099988672\n", "LOCAL TRAINING: Client #317, Epoch 2, #examples 352, Loss 3.087851517730289, Accuracy 0.17222222540941504\n", "LOCAL TRAINING: Client #317, Epoch 3, #examples 352, Loss 2.516394015815523, Accuracy 0.363888896173901\n", "LOCAL TRAINING: Client #366, Epoch 1, #examples 134, Loss 2.950592279434204, Accuracy 0.2321428619325161\n", "LOCAL TRAINING: Client #366, Epoch 2, #examples 134, Loss 2.4150586298533847, Accuracy 0.3535714325095926\n", "LOCAL TRAINING: Client #366, Epoch 3, #examples 134, Loss 2.0281652978488376, Accuracy 0.4428571531815188\n", "LOCAL TRAINING: Client #216, Epoch 1, #examples 159, Loss 3.5574139207601547, Accuracy 0.12569444766268134\n", "LOCAL TRAINING: Client #216, Epoch 2, #examples 159, Loss 2.8454615622758865, Accuracy 0.290277783293277\n", "LOCAL TRAINING: Client #216, Epoch 3, #examples 159, Loss 2.4366263896226883, Accuracy 0.4097222313284874\n", "LOCAL TRAINING: Client #8, Epoch 1, #examples 128, Loss 3.45980690075801, Accuracy 0.16153846509181535\n", "LOCAL TRAINING: Client #8, Epoch 2, #examples 128, Loss 2.852830171585083, Accuracy 0.2826923131942749\n", "LOCAL TRAINING: Client #8, Epoch 3, #examples 128, Loss 2.523867808855497, Accuracy 0.3961538557822888\n", "LOCAL TRAINING: Client #81, Epoch 1, #examples 138, Loss 3.7141885416848317, Accuracy 0.15357143059372902\n", "LOCAL TRAINING: Client #81, Epoch 2, #examples 138, Loss 3.0983834266662598, Accuracy 0.21428571747882025\n", "LOCAL TRAINING: Client #81, Epoch 3, #examples 138, Loss 2.7150428209986006, Accuracy 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Epoch 3, #examples 134, Loss 1.2844071984291077, Accuracy 0.6642857151372092\n", "LOCAL TRAINING: Client #2, Epoch 1, #examples 153, Loss 2.2797421365976334, Accuracy 0.36458334047347307\n", "LOCAL TRAINING: Client #2, Epoch 2, #examples 153, Loss 1.9956230074167252, Accuracy 0.5020833434537053\n", "LOCAL TRAINING: Client #2, Epoch 3, #examples 153, Loss 1.604733221232891, Accuracy 0.5500000081956387\n", "LOCAL TRAINING: Client #3, Epoch 1, #examples 154, Loss 2.4713069945573807, Accuracy 0.40625000838190317\n", "LOCAL TRAINING: Client #3, Epoch 2, #examples 154, Loss 2.1827912479639053, Accuracy 0.42500000447034836\n", "LOCAL TRAINING: Client #3, Epoch 3, #examples 154, Loss 1.8138206750154495, Accuracy 0.5000000093132257\n", "LOCAL TRAINING: Client #4, Epoch 1, #examples 156, Loss 2.9105244800448418, Accuracy 0.2812500046566129\n", "LOCAL TRAINING: Client #4, Epoch 2, #examples 156, Loss 2.2828214317560196, Accuracy 0.38958333898335695\n", "LOCAL TRAINING: Client #4, Epoch 3, 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0.44642857568604605\n", "LOCAL TRAINING: Client #366, Epoch 2, #examples 134, Loss 1.7017232775688171, Accuracy 0.5964285688740867\n", "LOCAL TRAINING: Client #366, Epoch 3, #examples 134, Loss 1.4425506038325173, Accuracy 0.6285714272941861\n", "[ 0 0 1 1 1 1 105 0 1 0 1 1 1 1 0 131 1 1\n", " 1 1 158 1 86 161 99 133 100 103 159 134 127 1 117 128 89 110\n", " 120 63 123 157 102 91 95 118 139 87 111 124 1 125 83 1 1 1\n", " 155 135 1 1 1 1 1 113 3 112 1 96 108 156 104 121 151 1\n", " 1 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 0 0 8 1 1 1 162 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 160 1 150 1 1 1 1 149 1 1 88\n", " 55 109 141 129 66 140 1 122 92 137 1 1 1 1 1 4 1 1\n", " 1 1 0 1 1 1 37 1 126 1 1 93 153 74 1 152 45 1\n", " 81 1 69 154 115 116 101 119 58 130 143 106 49 51 85 1 1 146\n", " 1 1 1 79 1 1 1 1 1 1 17 1 1 1 142 1 1 1\n", " 84 64 138 57 136 107 59 54 77 97 114 1 144 78 1 1 0 1\n", " 1 1 132 4 1 4 1 0 1 0 1 1 1 145 43 75 147 28\n", " 90 42 47 52 98 1 0 67 1 1 0 1 76 70 1 1 53 61\n", " 94 80 71 25 148 65 1 41 48 44 56 21 27 46 62 68 82 72\n", " 1 50 60 26 32 40 13 1 24 35 30 1 1 1 34 4 1 1\n", " 1 1 1 1 20 23 22 31 10 29 1 15 1 1 1 0 1 1\n", " 8 1 1 11 39 33 19 14 9 16 6 12 3 5 1 1 1 0\n", " 1 0 2 1 0 1 1 1 1 73 7 1 1 1 1 36 1 1\n", " 1 1 1 1 1 1 1 1 1 0 1 1 1 18 18 38 17 37]\n", "163\n", "Cluster 1.3 will not be split any further\n", "\n", "Running FCFL with 1 client idxs: [136]\n", "Running learning for 1 communication rounds\n", "\n", "Communication round 1\n", "LOCAL TRAINING: Client #136, Epoch 1, #examples 342, Loss 1.927261143071311, Accuracy 0.4971428628478731\n", "LOCAL TRAINING: Client #136, Epoch 2, #examples 342, Loss 1.302115706886564, Accuracy 0.654285718713488\n", "LOCAL TRAINING: Client #136, Epoch 3, #examples 342, Loss 0.811852034500667, Accuracy 0.7799999960831233\n", "CLUSTER: 1.4, MEAN TEST ACCURACY: 0.2068965584039688, 10th percentile: 0.2068965584039688, 90th percentile: 0.2068965584039688, % clients reaching target accuracy: 0.0\n", "GLOBAL WEIGHTS DELTA NORM: 3.9944217205047607\n", "718s elapsed\n", "Training complete in 1 communication rounds\n", "\n", "Running 1 round of training on all client in cluster to obtain client weight updates\n", "LOCAL TRAINING: Client #136, Epoch 1, #examples 342, Loss 0.6176639318466186, Accuracy 0.8171428561210632\n", "LOCAL TRAINING: Client #136, Epoch 2, #examples 342, Loss 0.457708801222699, Accuracy 0.8485714265278408\n", "LOCAL TRAINING: Client #136, Epoch 3, #examples 342, Loss 0.3266952888241836, Accuracy 0.9171428493091038\n", "Cluster 1.4 will not be split any further\n", "\n", "Running FCFL with 1 client idxs: [29]\n", "Running learning for 1 communication rounds\n", "\n", "Communication round 1\n", "LOCAL TRAINING: Client #29, Epoch 1, #examples 312, Loss 1.7692295890301466, Accuracy 0.559375002514571\n", "LOCAL TRAINING: Client #29, Epoch 2, #examples 312, Loss 1.144331632182002, Accuracy 0.6843750062398612\n", "LOCAL TRAINING: Client #29, Epoch 3, #examples 312, Loss 0.869970350060612, Accuracy 0.7749999994412065\n", "CLUSTER: 1.5, MEAN TEST ACCURACY: 0.1111111119389534, 10th percentile: 0.1111111119389534, 90th percentile: 0.1111111119389534, % clients reaching target accuracy: 0.0\n", "GLOBAL WEIGHTS DELTA NORM: 3.5797665119171143\n", "720s elapsed\n", "Training complete in 1 communication rounds\n", "\n", "Running 1 round of training on all client in cluster to obtain client weight updates\n", "LOCAL TRAINING: Client #29, Epoch 1, #examples 312, Loss 0.7012270533014089, Accuracy 0.8218749966472387\n", "LOCAL TRAINING: Client #29, Epoch 2, #examples 312, Loss 0.7168337660841644, Accuracy 0.7999999960884452\n", "LOCAL TRAINING: Client #29, Epoch 3, #examples 312, Loss 0.43774264375679195, Accuracy 0.8937499970197678\n", "Cluster 1.5 will not be split any further\n", "\n", "Running FCFL with 1 client idxs: [300]\n", "Running learning for 1 communication rounds\n", "\n", "Communication round 1\n", "LOCAL TRAINING: Client #300, Epoch 1, #examples 349, Loss 1.5152519907270159, Accuracy 0.6019047641328403\n", "LOCAL TRAINING: Client #300, Epoch 2, #examples 349, Loss 0.7154816286904472, Accuracy 0.8139682463237218\n", "LOCAL TRAINING: Client #300, Epoch 3, #examples 349, Loss 0.43131054086344583, Accuracy 0.8936507872172764\n", "CLUSTER: 1.6, MEAN TEST ACCURACY: 0.2222222238779068, 10th percentile: 0.2222222238779068, 90th percentile: 0.2222222238779068, % clients reaching target accuracy: 0.0\n", "GLOBAL WEIGHTS DELTA NORM: 3.6750009059906006\n", "722s elapsed\n", "Training complete in 1 communication rounds\n", "\n", "Running 1 round of training on all client in cluster to obtain client weight updates\n", "LOCAL TRAINING: Client #300, Epoch 1, #examples 349, Loss 0.2827529046684504, Accuracy 0.9028571367263794\n", "LOCAL TRAINING: Client #300, Epoch 2, #examples 349, Loss 0.25370586908289366, Accuracy 0.9257142782211304\n", "LOCAL TRAINING: Client #300, Epoch 3, #examples 349, Loss 0.12083263064601592, Accuracy 0.9625396779605321\n", "Cluster 1.6 will not be split any further\n", "FCFL completed\n" ] } ], "source": [ "experiment.run()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 4 }