Users are allowed to integrate their own components, including datasets, models, etc., into FederatedScope to conduct federated learning for specific applications.
Fderated learning algorithms are modularized and expressed via defining events and corresponding handlers for the participants.
Developers can flexibly enrich the exchanged data and participants’ behaviors, which is helpful for various real-world federated learning applications.
We have implemented state-of-the-art personalized federated learning methods, and the well-designed interfaces make the development of new methods easy.
Out-of-the-box HPO functionalities can save users from the tedious loop of model tuning, allowing them to focus on their innovations.
05/2022, Our paper on federated graph learning package is accepted at KDD’22. pdf
05/2022, FederatedScope v0.1.0, our first release, is available at GitHub.