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.
Yun-Qi Mi, Ze-Yu Hao, Guo-Shuai Zhao et al.· Proceedings of the 32nd ACM...· 2 citations
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
Dynamic Multi-Path Retrieval for KB-VQA (DMRAG) is proposed, which re-trieves candidates through multiple retrieval paths that capture complementary visual and semantic cues and performs Question-Adaptive Gated Fusion to balance contributions from different modalities according to the query’s information need.
Zeyu Song, Yimin Deng, Yu-Xin Zhang et al.· Proceedings of the Thirty-Fi...· 0 citations
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