Easy-to-use
Users are allowed to integrate their own components, including datasets, models, etc., into FederatedScope to conduct federated learning for specific applications.
Event-driven
Fderated learning algorithms are modularized and expressed via defining events and corresponding handlers for the participants.
Flexible&Extendable
Developers can flexibly enrich the exchanged data and participants’ behaviors, which is helpful for various real-world federated learning applications.
Personalization
We have implemented state-of-the-art personalized federated learning methods, and the well-designed interfaces make the development of new methods easy.
Auto-tuning
Out-of-the-box HPO functionalities can save users from the tedious loop of model tuning, allowing them to focus on their innovations.
Privacy Protection
Technologies, including differential privacy, encryption, multi-party computation, etc., are provided to enhance the strength of privacy protection.