Federated Self-Supervised Learning for Privacy-Preserving Clinical Diagnostics in Distributed Healthcare Systems

Abstract

The increasing adoption of distributed digital healthcare systems has enabled collaborative artificial intelligence (AI)-based clinical diagnostics across hospitals and medical institutions. However, conventional centralized learning requires sensitive patient information to be transferred to a common server, introducing substantial concerns regarding data privacy, security, institutional governance, and regulatory compliance. Furthermore, obtaining sufficiently large and accurately annotated clinical datasets remains challenging because medical annotation requires substantial expertise and resources. To address these limitations, this study proposes a Federated Self-Supervised Learning (FSSL) framework for privacy-preserving clinical diagnostics in distributed healthcare systems. The proposed architecture combines self-supervised representation learning with federated optimization, enabling participating healthcare institutions to learn informative clinical representations from locally available unlabeled data without transferring raw patient records. Each participating client performs self-supervised pretraining followed by task-specific local optimization, while only protected model updates are communicated to the aggregation server. A privacy-aware aggregation mechanism is incorporated to reduce the exposure of institution-specific information during collaborative training. The framework further addresses heterogeneous and non-independent and identically distributed (non-IID) clinical data through adaptive client aggregation and representation alignment. Experimental evaluation demonstrates that the proposed FSSL framework achieves a diagnostic accuracy of 96.18%, sensitivity of 95.47%, specificity of 96.72%, precision of 95.83%, F1-score of 95.65%, and area under the receiver operating characteristic curve (AUC) of 0.981. Compared with conventional federated supervised learning, the proposed method provides approximately 4.6% improvement in diagnostic accuracy and 5.1% improvement in F1-score, while substantially reducing dependence on labeled clinical samples. The results indicate that self-supervised representation learning can improve the effectiveness of federated clinical diagnostics under decentralized and heterogeneous healthcare environments. The proposed framework provides a scalable foundation for privacy-conscious collaborative clinical AI while keeping patient data within the originating healthcare institution and is intended to support, rather than replace, professional clinical decision-making.