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Continual Federated Learning (CFL) allows distributed devices tocollaboratively learn novel concepts from continuously shifting training datawhile avoiding knowledge forgetting of previously seen tasks. To tackle thischallenge, most current CFL approaches rely on extensive rehearsal of previousdata. Despite effectiveness, rehearsal comes at a cost to memory, and it mayalso violate data privacy. Considering these, we seek to apply regularizationtechniques to CFL by considering their cost-efficient properties that do notrequire sample caching or rehearsal. Specifically, we first apply traditionalregularization techniques to CFL and observe that existing regularizationtechniques, especially synaptic intelligence, can achieve promising resultsunder homogeneous data distribution but fail when the data is heterogeneous.Based on this observation, we propose a simple yet effective regularizationalgorithm for CFL named FedSSI, which tailors the synaptic intelligence for theCFL with heterogeneous data settings. FedSSI can not only reduce computationaloverhead without rehearsal but also address the data heterogeneity issue.Extensive experiments show that FedSSI achieves superior performance comparedto state-of-the-art methods.

Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence