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.
Few-shot class-incremental learning for classifi- cation and object detection: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025
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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.