STPFL: Stable teacher-guided personalized federated learning for heterogeneous multi-institution medical imaging

FedSCF: sensitivity-aware collaborative fusion for personalized federated learning in medical image classification

A PFL framework, FedSCF, which models client heterogeneity at the parameter level, including a relative perturbation-based sensitivity evaluation is designed to identify critical parameters for personalized modeling, while the remaining parameters participate in cross-client sharing.

Federated learning for medical image classification based on prototype alignment

One-Shot Federated Class-Incremental Learning for Medical Imaging via Variational Feature Transfer

A novel class-incremental continual learning model for a one-shot FL paradigm, in which each task introduces new classes, clients observe heterogeneous and evolving class distributions, and communication with the server occurs only once, substantially mitigates catastrophic forgetting while consistently enhancing recognition of newly introduced classes.

FedAD: adaptive federated disentanglement for domain generalization in unlabeled medical imaging

Three barriers significantly hinder the use of deep learning in medical imaging: poor generalization to new clinical domain shifts, label scarcity, and data privacy. A unified framework that learns from unlabeled, decentralized data while optimizing for generalization is desperately needed, even if Federated Learning (FL), Self-Supervised Learning (SSL), and Domain Generalization (DG) provide partial solutions that often operate under contradictory assumptions. We present FedAD: Adaptive Federated Disentanglement, a unique framework that uses two key ideas to handle these problems in a synergistic way. First, a federated semantic disentanglement objective (FedSD) explicitly distinguishes between the domain-invariant semantic characteristics and domain-specific variants using a non-adversarial orthogonality constraint. Second, in order to prevent premature convergence and enhance resilience, an adaptive teacher-student alignment (ATSA) curriculum dynamically modifies the generalization pressure based on the stability of the global model. This dual technique creates a strong feature encoder by forcing the model to learn what it sees as opposed to where it sees it. FedAD outperforms current approaches in terms of generalization to unseen target domains, as demonstrated by its validation on publicly available medical datasets. Our strategy concurrently addresses privacy, label scarcity, and domain change, paving the road for useful, reliable, and fair medical AI.

FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging

This work proposes a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients and introduces feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it into feature drift and prototype drift.

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

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.