A Data-aware Probabilistic Client Sampling Scheme in Streaming Federated Learning

In streaming federated learning, where data on each client is received in the form of a data stream, the distribution of data on the clients has a significant impact on the performance of the federated learning model. The continuous influx of streaming data on the clients leads to real-time changes in the local data distribution, which in turn affects the performance of the federated learning model. Furthermore, the heterogeneity in data distribution among clients exacerbates this impact. In this paper, to address these challenges, we propose a Data-aware Probabilistic Client Sampling scheme (DPCS) for selecting appropriate clients to participate in model training in each round of federated learning. DPCS begins with a method for real-time monitoring of local data distributions on the clients. Based on these observations, the central server adopts a probability-based client sampling strategy. Through extensive experimentation, we demonstrate that our client sampling scheme offers higher timeliness and enhances the performance of federated learning compared to traditional methods.