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<p>With the widespread application of Federated Learning (FL) in distributed data environments, enhancing system communication efficiency while maintaining model performance has become a critical challenge in this field. In this paper, we propose SaAS-FL, an innovative FL algorithm that aims to balance model accuracy and communication efficiency. First, it employs a synchronous training mode to obtain a relatively stable baseline global model. Then, it adopts an asynchronous update approach to achieve efficient aggregation, while introducing a delay factor based on client staleness to dynamically adjust aggregation weights, thereby mitigating the adverse effects of stale clients on model performance. Finally, an accuracy-based decision mechanism is employed to determine whether to update the global model, which avoids the distribution of ineffective models and effectively prevents model degradation. Experimental results demonstrate that SaAS-FL achieves high communication efficiency while maintaining high model accuracy, exhibiting strong robustness and adaptability across diverse heterogeneous data environments. This approach offers novel insights for enhancing FL efficiency.</p>

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An efficient aggregation algorithm based on synchronous-asynchronous mechanism for federated learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

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  • Yangcheng Mou,
  • Aiwang Chen,
  • Guirong Chen,
  • Jiming Xu,
  • Xiaomei Yan,
  • Wei Tang,
  • Lian Duan

摘要

With the widespread application of Federated Learning (FL) in distributed data environments, enhancing system communication efficiency while maintaining model performance has become a critical challenge in this field. In this paper, we propose SaAS-FL, an innovative FL algorithm that aims to balance model accuracy and communication efficiency. First, it employs a synchronous training mode to obtain a relatively stable baseline global model. Then, it adopts an asynchronous update approach to achieve efficient aggregation, while introducing a delay factor based on client staleness to dynamically adjust aggregation weights, thereby mitigating the adverse effects of stale clients on model performance. Finally, an accuracy-based decision mechanism is employed to determine whether to update the global model, which avoids the distribution of ineffective models and effectively prevents model degradation. Experimental results demonstrate that SaAS-FL achieves high communication efficiency while maintaining high model accuracy, exhibiting strong robustness and adaptability across diverse heterogeneous data environments. This approach offers novel insights for enhancing FL efficiency.