Book
Open access
Aug 2026
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.
Yunqi Mi, Zeyu Hao, Guoshuai Zhao et al.
· Proceedings of the 32nd ACM... · 1 citation