Weixi Zhang

FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space

This work proposes FlatLand, a novel personalized federated learning method that embeds different clients'data in tailored Lorentz space of hyperbolic geometry, and develops a parameter decoupling strategy that separates heterogeneous information from common knowledge, enabling direct aggregation without requiring client similarity estimation and extra calculation modules.