Mitigating Spatio-Temporal Forgetting in Federated Continual Learning with Dynamic-Domain Clients

Distributed sensing systems increasingly rely on geographically distributed devices that collect data under different operating conditions. Federated continual learning (FCL) enables a global model to be trained across distributed sensing clients whose local data evolve over time, while preserving previously acquired knowledge. Existing FCL studies have mainly addressed continual task evolution or class-based client heterogeneity, while federated domain-incremental learning has investigated adaptation to changing domains under a shared label space. Less attention has been paid to scenarios where continual task evolution and domain-shifted client participation occur simultaneously, leading to coupled temporal and spatial interference. In this paper, we study a dynamic-domain federated continual learning setting, where new clients share the same label space with source clients but introduce shifted feature distributions during training. This setting reveals a challenging form of spatio-temporal catastrophic forgetting: the model must learn evolving class-incremental tasks while preventing new-domain knowledge injection from degrading source-domain performance. To address this problem, we propose STP-FCL, a coordinated client-server framework. On the client side, STP-FCL preserves historical task semantics and source-domain discriminative structures through source-aware prototype-space optimization, reducing representation drift under evolving tasks and domains. On the server side, STP-FCL designs domain-projected aggregation to remove conflicting components from new-domain updates and adaptively control their fusion weight. Experiments under dynamic-domain federated continual learning scenarios show that STP-FCL achieves stronger source-domain knowledge retention and higher mixed-domain accuracy than representative baselines, with a trade-off in target-domain accuracy.