Rehearsal-Free Statistical Prototype Regularization for Federated Incremental Learning

Federated Incremental Learning (FIL) aims to learn sequential tasks across distributed clients under strict privacy constraints while mitigating catastrophic forgetting. However, existing methods often rely on storing, transmitting, or generating previous data to retrain the model, which not only introduces potential privacy risks but also incurs additional memory, communication, and computational costs, thereby limiting their deployment on resource-constrained edge devices. To tackle this challenge, we propose \textit{SPR-FIL}, a novel rehearsal-free and lightweight \underline{S}tatistical \underline{P}rototype \underline{R}egularization framework for \underline{F}ederated \underline{I}ncremental \underline{L}earning that leverages Gaussian statistics and preserves previous knowledge through aligned prototypes and subspace-constrained encoder updates. More specifically, the server aggregates client-side statistics into global prototypes and dynamically aligns historical prototypes with the evolving feature space through anchor-based moment matching. Locally, clients utilize these aligned prototypes to induce virtual posteriors which serve as soft supervision for distillation while encoder updates are further regularized by projecting gradients onto the orthogonal complement of covariance-induced historical knowledge subspaces. Extensive experiments demonstrate that SPR-FIL is model-agnostic, communication-efficient, and consistently outperforms state-of-the-art methods across various FIL settings, achieving improvements of up to 9.73\%.