
提案された寄与を考慮した非同期FL手法は、受信した更新の新鮮さと統計的異質性を考慮し、グローバルモデルへの貢献を動的に調整することで、収束速度を向上させます。


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federated-learning-contribution-aware-asynchronous-approach


Federated Learning: Contribution-Aware Asynchronous Approach


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FedFa, a fully asynchronous parameter update strategy for federated learning, can eliminate waiting time and guarantee convergence by merging historical model updates into the current update.


fully-asynchronous-training-paradigm-for-federated-learning-fedfa


Fully Asynchronous Training Paradigm for Federated Learning: FedFa



Asynchronous federated learning mechanisms can be significantly improved by modeling the queuing dynamics of the system, enabling non-uniform node sampling and better convergence guarantees.


optimizing-asynchronous-federated-learning-with-queuing-dynamics-analysis


Optimizing Asynchronous Federated Learning with Queuing Dynamics Analysis
