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Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
Zijian Wang, Xiaofei Zhang, Xin Zhang +2
Federated learning (FL) is increasingly adopted in domains like healthcare, where data privacy is paramount. A fundamental challenge in these systems is statistical heterogeneity-t…
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
Minghui Chen, Meirui Jiang, Xin Zhang +3
Federated learning (FL) is a learning paradigm that enables collaborative training of models using decentralized data. Recently, the utilization of pre-trained weight initializatio…
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Chun-Yin Huang, Kartik Srinivas, Xin Zhang +1
Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless n…