AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

With the advent of the 5G and 6G eras and the explosive growth of mobile users, machine learning (ML) is increasingly used for extracting important information from a large amount of generated data and making intelligent decisions for complex environments. Especially, distributed ML techniques are getting more attention to enable training ML models in a distributed manner by exploiting distributed computational resources at the network edge. Federated learning (FL) as a classical distributed learning approach can not only protect data privacy but also reduce communication overhead. However, it requires synchrony among users, which is hard to satisfy due to the heterogeneity of the wireless networks. Hence, we first propose a clustering-based semi-asynchronous online FL with Age-of-Update (AoU)-based local update (CSAOFL-ALU) with importance-based user clustering and asynchronous federated learning with AoU-based local update (AsynFL-ALU)-based local update. After that, the base station aggregates the cluster model of each cluster with synchronous FL. We also provide mathematical convergence analysis of the CSAOFL-ALU algorithm. The results show that the global model convergence rate is inversely proportional to the users' AoU, at the same time, the convergence bound of the global loss function is inversely proportional to the size and the importance of the user data set. The experiments are conducted on the nonindependently and identically distributed MINST data set. Numerical results demonstrate that the proposed AsynFL-ALU with priority-based user scheduling achieves better learning performance than fully asynchronous FL, and converges faster than the baseline user scheduling schemes. The CSAOFL-ALU converges faster with less communication time than the baseline algorithms and increases the fairness of user participation.