Zeyu Hao

CanonFedRec: A Canonical Geometric Framework for Personalized Federated Recommendation

CanonFedRec is proposed, a novel framework that achieves superior performance while reducing memory costs by up to 40× compared to the best FedRec approach and treats item-wise variance as a proxy for client-side cognitive divergence, and dynamically adapting optimization objectives via elastic decision boundaries.