Core features
Scalability and resilience
FEDn boosts federated learning by efficiently coordinating client models and aggregating them across several servers, catering to high client volumes and ensuring robust recovery from failures. It supports asynchronous training, smoothly handling client connectivity changes.
Security
FEDn enhances security in federated learning environments by eliminating the need for clients to open ingress ports and using standard encryption protocols and token authentication. This approach streamlines deployment across varied settings and ensures secure, easy integration.
Real-time monitoring and analysis
With comprehensive event logging and distributed tracing, FEDn enables real-time monitoring of events and training progress, facilitating easier troubleshooting and auditing. The API offers access to machine learning validation metrics from clients, allowing for detailed analysis of federated experiments.
Framework agnostic
FEDn is designed to be ML-framework agnostic, seamlessly supporting major frameworks such as Keras, PyTorch, and scikit-learn. Ready-to-use examples are provided, facilitating immediate application across different ML frameworks.
