Preserving privacy, enabling collaboration: Decentralized learning framework for multi-class orthopedic imaging

Abstract

Medical imaging data are inherently distributed across healthcare institutions and subject to strict privacy regulations, limiting the feasibility of centralized model training. In orthopedic imaging, further challenges arise from heterogeneous diagnostic tasks, implant categories, and label spaces that differ across institutions. Existing decentralized approaches, including federated and swarm learning, reduce direct data sharing but typically rely on repeated parameter synchronization and assume partially aligned label spaces, restricting their scalability in heterogeneous clinical environments. To address these limitations, we propose OrthoATD.Net, a decentralized learning framework for collaborative orthopedic image analysis that operates without raw-data sharing or iterative parameter synchronization. The framework combines independent local training with synchronization-free representation sharing, enabling knowledge integration across fully disjoint label spaces. We evaluate OrthoATD.Net across six heterogeneous orthopedic nodes comprising 43,976 X-ray images and 30 implant and diagnostic classes, using identical Vision Transformer backbones and leakage-controlled evaluation protocols. Over three independent runs, the framework achieves a mean accuracy of 97.32±0.03% and a macro F1-score of 96.45±0.07%. Within this heterogeneous disjoint-label setting, relative to the strongest decentralized baseline (Ditto-adapted, 92.93%), it improves accuracy by 4.39 and macro F1-score by 5.84 percentage points, and consistently outperforms NonIID-SL (91.18%), FedPer-adapted (90.82%), centralized learning (89.27%), FedLD (87.25%), and ATD (71.21%) under identical experimental conditions. Multi-seed statistical validation with significance testing, leave-one-node-out generalization analysis, and membership-inference attack analysis further demonstrate the robustness, reproducibility, and practical viability of the framework. The primary contribution of OrthoATD.Net is enabling synchronization-free collaborative learning across heterogeneous clinical nodes with fully disjoint label spaces rather than establishing a universal performance advantage over centralized learning. These findings suggest that synchronization-free representation sharing can serve as an effective and scalable alternative to conventional decentralized learning for heterogeneous orthopedic imaging tasks while preserving data locality, providing a promising basis for privacy-aware, scalable collaborative orthopedic artificial intelligence across distributed healthcare environments.