On the Local Cache Update Rules in Streaming Federated Learning

In this study, we address the emerging field of streaming federated learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity. Traditional federated learning (FL) relies on fixed data sets, whereas in SFL, data is streamed, and its distribution changes over time, leading to discrepancies between the local training data set and long-term distribution. To mitigate this problem, we propose three local cache update rules—first-in–first-out (FIFO), static ratio selective replacement (SRSR), and dynamic ratio selective replacement (DRSR)—that update the local cache of each client while considering the limited cache capacity. Furthermore, we derive a convergence bound for our proposed SFL algorithm as a function of the distribution discrepancy between the long-term data distribution and the client’s local training data set. We then evaluate our proposed algorithm on two data sets: 1) a network traffic classification data set and 2) an image classification data set. Our experimental results demonstrate that our proposed local cache update rules significantly reduce the distribution discrepancy and outperform the baseline methods. Our study advances the field of SFL and provides practical cache management solutions in FL.