Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated learning methods must be able to operate reliably. Besides varying amounts of non-IID data at participants, the asynchronous setting models heterogeneous client participation due to available computational power and battery constraints and also accounts for delayed communications between clients and the server. To reduce the communication overhead associated with asynchronous online federated learning (ASO-Fed), we use the principles of partial-sharing-based communication. In this manner, we reduce the communication load of the participants and, therefore, render participation in the learning task more accessible. We prove the convergence of the proposed ASO-Fed and provide simulations to analyze its behavior further. The simulations reveal that, in the asynchronous setting, it is possible to achieve the same convergence as the federated stochastic gradient (Online-FedSGD) while reducing the communication tenfold.

翻译：联合会学习文献中的许多假设都提出了在大多数现实世界应用中无法满足的最佳假设。一个不同步的环境反映了联合会学习方法必须能够可靠运作的现实环境。除了参与者的不同数量非IID数据外,由于现有的计算功率和电池限制以及客户与服务器之间通信延迟的原因,非同步的设置模式客户参与情况也各不相同。为了减少与非同步在线联合学习(ASO-Feded)有关的通信间接费用,我们采用了部分共享通信的原则。这样,我们减少了参与者的通信负荷,从而使参与学习任务的人更易于参与。我们证明拟议的ASO-Fed的趋同,并提供模拟来进一步分析其行为。模拟表明,在无同步的环境中,在减少通信十倍的同时,有可能实现与联合式在线共享梯(Online-FedSGD)相同的趋同。