Digital Twin-Enabled Edge Federated Learning for Data Streams

With the significant advancement in the Internet of Things (IoT), Streaming Federated Learning (SFL) as a novel distributed learning approach can deal with time-varying streaming data among multiple sources. Standard SFL protocol is a collaborative training framework that enables many clients bounded with different online data sources to participate in a continuous training task. However, existing works ignore the cold-start problem and insufficient training data obstacle. Besides, due to the client heterogeneity and forgetting problem, the global model faces performance degradation during the time-series streaming data. In our work, we propose a digital twin-enabled SFL, a novel federated learning system with digital twin support to augment training data on demand. Instead of adopting an asynchronous federated learning protocol or buffer technique to wait for clients to have enough data, Generative adversarial network-based digital twins are introduced to construct a virtual replica for each federated learning client to generate a synthetic dataset based on the real data stream. We conduct the experiments using real-world datasets to evaluate the proposed SFL framework. The results under multiple data stream scenarios and various client behaviors demonstrate that our work outperforms the state-of-the-art baseline.