In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each agent independently chooses when to participate throughout the algorithm and the specific subset of its neighbourhood with which it will cooperate at any given moment. When an agent chooses to take part, it undergoes multiple local updates before conveying its outcomes to the sub-sampled neighbourhood. Under this setup, we prove that the resulting asynchronous diffusion strategy is stable in the mean-square error sense and provide performance guarantees specifically for the federated learning setting. We illustrate the findings with numerical simulations.

翻译：本文研究了一个异步运行的多智能体网络，旨在发现一个适用于各智能体本地数据集的理想全局模型。我们假设每个智能体独立选择参与算法的时间以及其在任意时刻与邻域子集进行协作的具体子集。当智能体选择参与时，它会在将结果传递给子采样邻域之前执行多次局部更新。在此设置下，我们证明了所提出的异步扩散策略在均方误差意义下是稳定的，并特别针对联邦学习场景提供了性能保证。最后，我们通过数值仿真验证了研究结论。