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Federated learning in healthcare: A comprehensive survey on privacy, scalability and clinical applications
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DOI：10.1016/j.icte.2026.05.011.png)
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Abstract
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The growing accessibility of medical data, coupled with advances in artificial intelligence, has accelerated the development of data-driven healthcare solutions. Deep learning models are highly effective in this context, as they are inherently designed to leverage data-driven approaches, with performance improving as larger datasets become available. While these models have demonstrated significant success, reliance on centralized data aggregation raises concerns regarding patient privacy, data heterogeneity, communication overhead, and system scalability. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training across institutions while preserving data confidentiality. This survey presents a comprehensive review of 80 studies published between 2020 and 2026, highlighting the state of the art in FL applications for healthcare. It classifies research across multiple dimensions, including standard FL variants and aggregation strategies, communication-efficient methods, peer-to-peer FL, blockchain-based frameworks, explainable AI integration, deep learning and reinforcement learning architectures, hybrid optimization approaches, and specialized healthcare applications. Additionally, the survey examines recent directions in vertical and split FL, FL frameworks and platforms, and the integration of large language models (LLMs) and retrieval-augmented generation (RAG) within FL systems. Through this structured analysis, key challenges such as privacy risks, interoperability, communication bottlenecks, and lack of clinical validation are identified and mapped to potential solutions. Finally, a research roadmap is provided, outlining future directions in privacy-preserving techniques, scalability, data standardization, and clinical adoption. By bridging technological advances with healthcare requirements, this survey offers researchers and practitioners a concise reference for advancing secure, scalable, and intelligent healthcare systems through federated learning.
Keywords:
Federated learning
Artificial intelligence
Privacy preservation
Deep learning
Healthcare
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