FEAT mitigates representation collapse and prediction bias in federated continual learning by aligning feature angular similarities to shared Equiangular Tight Frame prototypes and removing task-irrelevant directional components from embeddings.
Global prompt re- finement with non-interfering attention masking for one-shot federated learning
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From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
FEAT mitigates representation collapse and prediction bias in federated continual learning by aligning feature angular similarities to shared Equiangular Tight Frame prototypes and removing task-irrelevant directional components from embeddings.