FedOPS: Order-sensitive Parameter Sanitization for cross‑client Federated Learning in Medical Image Segmentation

Recently, cross-client federated learning has received growing attention in medical image segmentation, owing to its superior performance and improved security guarantees compared with conventional server-based federated learning paradigms. However, existing methods suffer from two key challenges: 1) Severe data heterogeneity (non-IID) across clients often causes conflicting gradient directions during parameter exchanges, resulting in training oscillations that degrade convergence rate and final segmentation accuracy. 2) Critical parameter contamination during parameter transmission may cause interference parameters to overwhelm critical ones, resulting in incorrect pixel predictions. To overcome these issues, we propose a novel Order-sensitive Parameter Sanitization (FedOPS) framework for decentralized federated foundation models, which ensures more secure and stable training. Specifically, to promote stable convergence and effective knowledge propagation between different clients, we develop a consistency-aware client scheduling (CCS) strategy that orders clients based on feature representation similarity. To prevent critical parameter contamination, we introduce a cross-client parameter sanitization (CPS) mechanism that leverages historical update directions to selectively discard interfering updates. Extensive experiments on multiple benchmarks demonstrate that FedOPS consistently outperforms existing methods, highlighting its effectiveness for federated medical image segmentation.