With the advent of the IoT, AI and ML/DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. Consequently, the realm of data-driven medical applications has garnered significant attention spanning academia and industry, ushering in marked enhancements in healthcare delivery quality. Despite these strides, the adoption of AI-driven medical applications remains hindered by formidable challenges, including the arduous task of meeting security, privacy, and quality of service (QoS) standards. Recent developments in federated learning have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. We delve into the contemporary research trends and their outcomes, unravelling the intricacies of designing reliable and scalable FL models. Our survey outlines the foundational statistical predicaments of FL, confronts device-related obstacles, delves into security challenges, and navigates the intricate terrain of privacy concerns, all while spotlighting its transformative potential within the medical domain. A primary focus of our study rests on medical applications, where we underscore the weighty burden of global cancer and illuminate the potency of FL in engendering computer-aided diagnosis tools that address this challenge with heightened efficacy.

翻译：随着物联网、人工智能和机器学习/深度学习算法的兴起，数据驱动的医学应用领域已成为从医疗数据中构建稳健且可扩展的诊断与预测模型的一个有前景的途径。因此，数据驱动的医学应用引起了学术界和工业界的广泛关注，显著提升了医疗服务质量。尽管取得了这些进展，人工智能驱动的医学应用仍受到严峻挑战的制约，包括难以满足安全、隐私和服务质量（QoS）标准。联邦学习的最新进展使得以分布式方式训练复杂的机器学习模型成为可能，并已成为一个活跃的研究领域，特别是在网络边缘以去中心化方式处理医疗数据以保护隐私和解决安全问题方面。为此，本综述论文重点探讨了联邦学习技术在医学应用中的现状与未来，在这些应用中，数据共享是一个重大负担。我们深入研究了当代研究趋势及其成果，揭示了设计可靠且可扩展的联邦学习模型的复杂性。我们的综述概述了联邦学习的基本统计困境，直面与设备相关的障碍，深入探讨了安全挑战，并导航了隐私问题的复杂领域，同时突显了其在医学领域中的变革潜力。我们的研究主要聚焦于医学应用，强调了全球癌症的沉重负担，并阐明了联邦学习在生成计算机辅助诊断工具以更高效地应对这一挑战方面的潜力。