Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing. Model-heterogeneous FL (MHFL) extends this paradigm by allowing clients to train personalized models with heterogeneous architectures tailored to their computational resources and application-specific needs. However, existing MHFL methods predominantly rely on centralized aggregation, which introduces scalability and efficiency bottlenecks, or impose restrictions requiring partially identical model architectures across clients. While peer-to-peer (P2P) FL removes server dependence, it suffers from model drift and knowledge dilution, limiting its effectiveness in heterogeneous settings. To address these challenges, we propose FedSKD, a novel MHFL framework that facilitates direct knowledge exchange through round-robin model circulation, eliminating the need for centralized aggregation while allowing fully heterogeneous model architectures across clients. FedSKD's key innovation lies in multi-dimensional similarity knowledge distillation, which enables bidirectional cross-client knowledge transfer at batch, pixel/voxel, and region levels for heterogeneous models in FL. This approach mitigates catastrophic forgetting and model drift through progressive reinforcement and distribution alignment while preserving model heterogeneity. Extensive evaluations on fMRI-based autism spectrum disorder diagnosis and skin lesion classification demonstrate that FedSKD outperforms state-of-the-art heterogeneous and homogeneous FL baselines, achieving superior personalization (client-specific accuracy) and generalization (cross-institutional adaptability). These findings underscore FedSKD's potential as a scalable and robust solution for real-world medical federated learning applications.

翻译：联邦学习（FL）使得无需直接共享数据的隐私保护协同模型训练成为可能。模型异构联邦学习（MHFL）扩展了这一范式，允许客户端根据其计算资源和特定应用需求，训练具有异构架构的个性化模型。然而，现有的MHFL方法主要依赖于中心化聚合，这带来了可扩展性和效率瓶颈，或者施加了要求客户端间部分模型架构必须相同的限制。虽然点对点（P2P）FL消除了对服务器的依赖，但它存在模型漂移和知识稀释的问题，限制了其在异构环境中的有效性。为了应对这些挑战，我们提出了FedSKD，一种新颖的MHFL框架，它通过轮询模型循环促进直接知识交换，消除了对中心化聚合的需求，同时允许客户端间采用完全异构的模型架构。FedSKD的核心创新在于多维相似性知识蒸馏，它能够在FL中为异构模型实现批处理、像素/体素和区域级别的双向跨客户端知识转移。该方法通过渐进式强化和分布对齐来缓解灾难性遗忘和模型漂移，同时保持模型异构性。在基于fMRI的自闭症谱系障碍诊断和皮肤病变分类上的广泛评估表明，FedSKD优于最先进的异构和同构FL基线方法，实现了卓越的个性化（客户端特定准确率）和泛化能力（跨机构适应性）。这些发现凸显了FedSKD作为现实世界医学联邦学习应用的可扩展且鲁棒的解决方案的潜力。