Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap

Federated Continual Learning (FCL) leverages inter-client collaboration to balance new knowledge acquisition and old knowledge retention, assuming explicit task boundaries. However, existing FCL methods struggle in streaming scenarios where ephemerally accessible data chunks exhibit arbitrary class overlap and lack task identifiers, inducing knowledge conflicts of the same class and preventing continual inference on local cumulative classes. To address this, we propose FedKACE with three synergistic components: 1) a gradient-responsive replay scheme that utilizes the ratio of squared L2 gradient norms of buffer to new samples to achieve client-specific balance between acquisition and retention; 2) a holistic buffer maintenance strategy that retains boundary-critical samples to enhance knowledge retention under class overlap; 3) an adaptive local-to-global inference model switching mechanism for continual inference on local cumulative classes. Experiments under diverse class overlap settings, along with theoretical analysis, demonstrate the effectiveness of FedKACE.