mlsanityData to 7 Oct 2026

Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions

arXiv 2502.19849 ↗First posted 27 Feb 2025cs.LGcited by 4

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Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning paradigm enabling collaborative model training across decentralized clients while preserving data privacy. In this paper, we revisit the stability of the vanilla FedAvg algorithm under diverse conditions. Despite its conceptual simplicity, FedAvg exhibits remarkably stable performance compared to more advanced FL techniques. Our experiments assess the performance of various FL methods on blood cell and skin lesion classification tasks using Vision Transformer (ViT). Additionally, we evaluate the impact of different representative classification models and analyze sensitivity to hyperparameter variations. The results consistently demonstrate that, regardless of dataset, classification model employed, or hyperparameter settings, FedAvg maintains robust performance. Given its stability, robust performance without the need for extensive hyperparameter tuning, FedAvg is a safe and efficient choice for FL deployments in resource-constrained hospitals handling medical data. These findings underscore the enduring value of the vanilla FedAvg approach as a trusted baseline for clinical practice.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 30 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

22 of 30 references.

References · 22

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10 more
  1. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  3. 2022DaViT: Dual Attention Vision Transformerscited by 174
  4. 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
  5. 2021FedCM: Federated Learning with Client-level Momentumcited by 77
  6. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  7. 2023FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracycited by 49
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imagingcited by 3
  2. 2025Debunking Optimization Myths in Federated Learning for Medical Image Classificationcited by 3
  3. 2025When to Stop Federated Learning: Zero-Shot Generation of Synthetic Validation Data with Generative AI for Early Stoppingcited by 1
  4. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0