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PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

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arxiv 2312.04992 v2 pith:QRKBPSLX submitted 2023-12-08 cs.LG cs.DC

Jianqing Zhang , Yang Liu , Yang Hua , Hao Wang , Tao Song , Zhengui Xue , Ruhui Ma , Jian Cao This is my paper · ORCID
classification cs.LGcs.DC
keywords learningpfllibalgorithmspersonalizedbenchmarkcomprehensivefederatedgained
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Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL)has gained significant prominence as a research direction within the FL domain. Whereas traditional FL (tFL) focuses on jointly learning a global model, pFL aims to balance each client's global and personalized goals in FL settings. To foster the pFL research community, we started and built PFLlib, a comprehensive pFL library with an integrated benchmark platform. In PFLlib, we implemented 37 state-of-the-art FL algorithms (8 tFL algorithms and 29 pFL algorithms) and provided various evaluation environments with three statistically heterogeneous scenarios and 24 datasets. At present, PFLlib has gained more than 1600 stars and 300 forks on GitHub.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FedRIR: Rethinking Information Representation in Federated Learning

    cs.LG 2025-02 conditional novelty 6.0 of 10

    FedRIR improves personalized federated learning by separating client-specific and global features with masked reconstruction and mutual information minimization, achieving up to 3.93% higher accuracy than prior methods.

  2. PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning

    cs.LG 2025-02 reject novelty 5.0 of 10

    PM-MoE adds a mixture-of-experts layer over all clients' converged personalized modules, with an energy-based denoiser, and reports modest accuracy improvements across six datasets.

  3. pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data

    cs.LG 2025-01 reject novelty 5.0 of 10

    pFedWN combines channel-aware neighbor selection with an EM-based model weighting step to personalize federated learning over server-free D2D wireless networks.

  4. Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning

    cs.LG 2025-01 reject novelty 5.0 of 10

    Delphi attacks federated learning by optimising first-layer weights to maximise predictive uncertainty, with Bayesian optimisation outperforming a trust-region variant.

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