0.5.0 - Initial Release Candidate

This is the initial release candidate of Syft 0.5.0, which returns feature parity back to our FL worker libraries.

KotlinSyft

Support for better error handling along with namespace changes

Static Federated Learning

KotlinSyft

KotlinSyft makes it easy for you to train and inference PySyft models on Android devices. This allows you to utilize training data located directly on the device itself, bypassing the need to send a user's data to a central server. This is known as federated learning.

• ⚙️ Training and inference of any PySyft model written in PyTorch or TensorFlow
• 👤 Allows all data to stay on the user's device
• ⚡ Support for full multi-threading / background service execution
• 🔑 Support for JWT authentication to protect models from Sybil attacks
• 👍 A set of inbuilt best practices to prevent apps from over using device resources.
  • 🔌 Charge detection to allow background training only when device is connected to charger
  • 💤 Sleep and wake detection so that the app does not occupy resource when user starts using the device
  • 💸 Wifi and metered network detection to ensure the model updates do not use all the available data quota
  • 🔕 All of these smart defaults are easily are overridable

Warm-up party

This is a base release tag to setup the project. Nothing to use here yet.