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FedCM: Federated Learning with Client-level Momentum

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arxiv 2106.10874 v1 pith:YY4SDR45 submitted 2021-06-21 cs.LG

Jing Xu , Sen Wang , Liwei Wang , Andrew Chi-Chih Yao This is my paper · ORCID
classification cs.LG
keywords fedcmfederatedlearningclientclient-levelgradientheterogeneitymomentum
verification ladder T0 review T1 audit T2 compute T3 formal
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Federated Learning is a distributed machine learning approach which enables model training without data sharing. In this paper, we propose a new federated learning algorithm, Federated Averaging with Client-level Momentum (FedCM), to tackle problems of partial participation and client heterogeneity in real-world federated learning applications. FedCM aggregates global gradient information in previous communication rounds and modifies client gradient descent with a momentum-like term, which can effectively correct the bias and improve the stability of local SGD. We provide theoretical analysis to highlight the benefits of FedCM. We also perform extensive empirical studies and demonstrate that FedCM achieves superior performance in various tasks and is robust to different levels of client numbers, participation rate and client heterogeneity.

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Cited by 9 Pith papers

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

  1. SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity

    cs.LG 2026-07 conditional novelty 7.0 of 10

    Inter-client gradient divergence in federated learning concentrates in low-frequency components; suppressing them via spectral or spatial high-pass filtering reduces client drift and raises accuracy under non-IID data.

  2. FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

    cs.LG 2026-07 conditional novelty 6.5 of 10

    Low-frequency components of client-side SAM perturbations carry most inter-client disagreement; high-pass filtering them yields more consistent federated updates and higher accuracy under non-IID data.

  3. FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity

    cs.LG 2026-07 conditional novelty 6.5 of 10

    Global-aware coordinate trust modulation after corrected AdamW updates improves federated Transformer and LLM training under data heterogeneity over strong adaptive baselines.

  4. FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios

    cs.LG 2025-07 reject novelty 6.0 of 10

    FedWCM uses per-client data-distribution scores to adapt momentum and aggregation weights in federated learning, showing empirical gains over FedAvg and FedCM on long-tailed non-IID datasets, but its convergence proof...

  5. Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Hybrid Batch Normalisation improves federated learning accuracy by combining local batch statistics with global statistics, and derives those global statistics from the pre-update global model.

  6. FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization

    cs.LG 2025-06 conditional novelty 5.0 of 10

    FedWSQ applies weight standardization in federated learning and uses Gaussian-optimal non-uniform quantization with a shared global scaling vector, improving accuracy at very low bit rates.

  7. Generalizable Federated Learning using Client Adaptive Focal Modulation

    cs.CV 2025-08 reject novelty 4.0 of 10

    The abstract describes AdaptFED, a claimed federated learning method, but the full text is an unrelated graph theory paper, so the claimed results are absent from the submission.

  8. pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization

    cs.DC 2025-06 reject novelty 4.0 of 10

    pFedSOP combines Gompertz-weighted local/global gradients with a rank-one Fisher Information Matrix update to speed up personalized federated learning, but the convergence proof is invalid and the update reduces to no...

  9. Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization

    cs.LG 2025-09 conditional novelty 3.0 of 10

    A PhD dissertation showing unified compression theory, personalized accelerated local training, and pruning methods that reduce communication costs in federated learning and maintain accuracy in LLM pruning.

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