1 citations · 1 across the 4 of their papers we have counts for
4 papers
Efficient Dataset Distillation for Pre-Trained Self-Supervised Models via Statistical Flow Matching
Qianxin Xia, Jiawei Du, Xin Zhang +6
Dataset distillation seeks to synthesize a compact surrogate dataset that enables performance comparable to training on the original dataset for downstream tasks. For the scenario…
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…