Decoupled in Time, Aligned in Space: Geometric Alignment for Temporally Heterogeneous Federated Continual Learning

Federated Continual Learning (FCL) extends federated learning to evolving local data streams, but existing methods largely assume comparable task progression across clients. In realistic edge environments, heterogeneous task workloads cause clients to advance through continual learning at different rates, producing persistent temporal misalignment across learning states. This misalignment biases global aggregation and progressively distorts previously accumulated knowledge. We formalize this problem as ***temporal heterogeneity*** and propose **TARDIS** (**T**emporal-**A**nchored **R**epresentation **A**lignment for **D**rift-**I**nhibiting **S**tabilization). TARDIS progressively constructs a global Equiangular Tight Frame (ETF) as continual knowledge expands, providing shared geometric anchors that maintain representational consistency across heterogeneous client states. Extensive experiments on four continual-learning benchmarks show that TARDIS consistently outperforms representative FCL baselines under both Task-IL and Class-IL settings, with particularly strong gains in global-class prediction and knowledge retention.