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Overcoming Forgetting in Federated Learning on Non-IID Data

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arxiv 1910.07796 v1 pith:E5K3DVSV submitted 2019-10-17 cs.LG cs.CRstat.ML

Neta Shoham (Edgify) , Tomer Avidor (Edgify) , Aviv Keren (Edgify) , Nadav Israel (Edgify) , Daniel Benditkis (Edgify) , Liron Mor-Yosef (Edgify) , Itai Zeitak (Edgify) This is my paper
classification cs.LGcs.CRstat.ML
keywords learningfederatedforgettinglocalmodelsadaptaddinganalogy
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We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show that this can be done efficiently for communication (adding no further privacy risks), scaling with the number of nodes in the distributed setting. Our experiments show that this method is superior to competing ones for image recognition on the MNIST dataset.

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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. Benchmark Evaluation of Federated Learning on Multi-organ Images

    cs.CV 2026-07 conditional novelty 6.0 of 10

    MobenFL is the broadest federated medical-imaging benchmark to date, pairing 20 algorithms with 22 multi-organ datasets and adding efficiency plus privacy metrics.

  2. SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation

    cs.LG 2025-07 conditional novelty 5.0 of 10

    SFedKD uses discrepancy-weighted multi-teacher knowledge distillation and greedy teacher selection to reduce catastrophic forgetting in sequential federated learning.

  3. Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning

    cs.LG 2025-06 conditional novelty 5.0 of 10

    FedBE appends zero-initialized transformer blocks to selected layers and allocates them across clients by resource and data profiles, reporting 12-74% better knowledge retention and 1.9-3.1x faster convergence in fede...

  4. Accelerated Training of Federated Learning via Second-Order Methods

    cs.LG 2025-05 conditional novelty 3.0 of 10

    A survey that categorizes second-order federated learning methods and argues they reduce communication rounds, based on results borrowed from the cited papers rather than new experiments.

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