A Multi-Model Approach for Handling Concept Drifting Data in Federated Learning

Federated learning (FL) is a distributed machine learning framework that trains a global model with local model updates from multiple client devices. When learning from data streams, the performance of FL models can be significantly degraded, because of concept drift and its subsequent issue of data heterogeneity. Concept drift refers to changes in data distribution. Concept drift in FL can be temporal and spatial, leading to outdated models under new data distributions and poorer model performance from exacerbated heterogeneity. We propose a multi-model approach (FedMCD) that keeps two local models at each client to balance the global and local performance discrepancy caused by concept drift. FedMCdalso integrates drift detection and an adaptation mechanism. We perform an extensive range of experiments to test the effectiveness through 17 artificial scenarios and 3 real-world datasets. We demonstrate that FedMCD achieves the highest accuracy over time in 16 out of 17 scenarios and presents the fastest performance recovery after drifts. It also significantly improved average accuracy on real-world data compared to six other drift-handling approaches.