Medical federated learning with joint graph purification for noisy label learning

Zhen Chen, Wuyang Li, Xiaohan Xing, Yixuan Yuan

Abstract: Highlights•We present a federated framework with joint graph purification (FedGP) to handle label noise in medical federated learning (FL) through server and clients collaboration. To the best of our knowledge, this work represents the first effort to explore the label noise in medical FL.•At the client side, noisy graph purification is devised to generate reliable pseudo labels for noisy samples, by progressively expanding the purified graph from reliable clean samples. Moreover, a graph-guided negative ensemble (GNE) loss is proposed to complement robust supervision of noisy samples with the purified graph.•At the server side, global centroid aggregation is devised to integrate the global knowledge across the FL framework to combat label noise under decentralized constraints. Through the collaborative optimization between server and clients, this strategy can produce a robust classifier with global knowledge against the FL label noise.•Extensive experiments are conducted on public endoscopic and pathological images under the homogeneous, heterogeneous, and real-world label noise for medical FL. Our FedGP framework outperforms denoising and noisy FL state-of-the-arts under diverse FL noise settings by a large margin.