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.
News (more)
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02/2023, Our paper elaborating on FederatedScope is accepted at VLDB’23. pdf
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10/2022, Our paper on personalized federated learning benchmark, pFL-Bench, is accepted at NeurIPS’22. pdf
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08/2022, Our KDD 2022 paper on federated graph learning receives the KDD Best Paper Award for ADS track!
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07/2022, We are hosting the CIKM’22 AnalytiCup competition. For more details, please see link
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06/2022, We release pFL-Bench, a comprehensive benchmark for personalized Federated Learning (pFL), containing 10+ datasets and 20+ baselines. GitHub, pdf
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06/2022, We release FedHPO-B, a benchmark suite for studying federated hyperparameter optimization. GitHub, pdf
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06/2022, We release B-FHTL, a benchmark suit for studying federated hetero-task learning. GitHub, pdf
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05/2022, Our paper on federated graph learning package is accepted at KDD’22. pdf
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05/2022, FederatedScope v0.1.0, our first release, is available at GitHub.