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cs.LG2026
Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients
Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao +5
Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parame…
cs.LG2026
Gradual Domain Adaptation for Graph Learning
Pui Ieng Lei, Ximing Chen, Yijun Sheng +3
Existing machine learning literature lacks graph-based domain adaptation techniques capable of handling large distribution shifts, primarily due to the difficulty in simulating a c…
cs.LG2024
HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning
Zhuoning Guo, Duanyi Yao, Qiang Yang +1
Federated Graph Learning (FGL) has emerged as a promising way to learn high-quality representations from distributed graph data with privacy preservation. Despite considerable effo…