June 5, 2024, 4:44 a.m. | Seongyoon Kim, Minchan Jeong, Sungnyun Kim, Sungwoo Cho, Sumyeong Ahn, Se-Young Yun

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02355v1 Announce Type: cross
Abstract: Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with heterogeneous, non-iid data distribution. A key challenge in FL is client drift, where data heterogeneity impedes the aggregation of scattered knowledge. Recent studies have tackled the client drift issue by identifying significant divergence in the last classifier layer. To mitigate this divergence, strategies such as freezing the classifier weights …

abstract aggregation arxiv challenge client cs.ai cs.cv cs.dc cs.lg data development distillation distribution drift feature federated learning framework global key personalized pivotal regression type

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