FedFMX Boosts Federated Class-Incremental Learning with Expert Routing.

Federated Learning (FL) is a promising distributed machine learning paradigm, but it faces significant hurdles when applied to class-incremental learning scenarios. These challenges include the shared model becoming overloaded and suffering from catastrophic forgetting, the inherent heterogeneity of non-IID data across clients, and issues with synchronized class alignment. To tackle these problems, researchers propose FedFMX (Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning). This framework enables adaptive expert specialization among different clients. The core idea is to route each data sample to a specific subset of experts, optimizing both the acquisition of new knowledge and the retention of existing knowledge. FedFMX incorporates a Fisher-Routed Expert Scoring (FRES) module to assess expert importance based on stability cost and plasticity gain. An Adaptive Expert Selection (AES) module then quantifies marginal contributions to determine the optimal expert subset. Finally, routing-aware regularization ensures load balance and efficient FL training. Theoretical analysis confirms an O(T^-1) convergence rate, and extensive experiments show FedFMX outperforms state-of-the-art methods.