easy-to-use

Easy-to-use

Users are allowed to integrate their own components, including datasets, models, etc., into FederatedScope to conduct federated learning for specific applications.

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event-driven architecture

Event-driven

Fderated learning algorithms are modularized and expressed via defining events and corresponding handlers for the participants.

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flexible&extendible

Flexible&Extendable

Developers can flexibly enrich the exchanged data and participants’ behaviors, which is helpful for various real-world federated learning applications.

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personalization

Personalization

We have implemented state-of-the-art personalized federated learning methods, and the well-designed interfaces make the development of new methods easy.

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auto-tuning

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.

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privacy-protection

Privacy Protection

Technologies, including differential privacy, encryption, multi-party computation, etc., are provided to enhance the strength of privacy protection.

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News (more)

  • 10/2022, Our paper on personalized federated learning benchmark, pFL-Bench, is accepted at NeurIPS’22. pdf

  • 08/2022, Our KDD 2022 paper on federated graph learning receives the KDD Best Paper Award for ADS track!

  • 07/2022, We are hosting the CIKM’22 AnalytiCup competition. For more details, please see link

  • 06/2022, We release pFL-Bench, a comprehensive benchmark for personalized Federated Learning (pFL), containing 10+ datasets and 20+ baselines. GitHub, pdf

  • 06/2022, We release FedHPO-B, a benchmark suite for studying federated hyperparameter optimization. GitHub, pdf

  • 06/2022, We release B-FHTL, a benchmark suit for studying federated hetero-task learning. GitHub, pdf

  • 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.