With the advent of the IoT, AI, ML, and 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. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have 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, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.

翻译：随着物联网、人工智能、机器学习与深度学习算法的出现，基于医疗数据设计稳健且可扩展的诊断与预后模型的数据驱动型医疗应用前景广阔。这一领域已引起学术界与工业界的广泛关注，显著提升了医疗服务质量。然而，AI驱动型医疗应用的推广仍面临严峻挑战，包括满足安全性、隐私性及服务质量等标准。联邦学习的最新进展使得以分布式方式训练复杂机器学习模型成为可能，并已成为活跃的研究领域，尤其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，本文探讨了联邦学习技术在数据共享面临重大挑战的医疗应用中的现状与未来。我们深入分析了当前研究趋势及其成果，揭示了设计可靠且可扩展的联邦学习模型的复杂性。本文概述了联邦学习中的基本统计问题，解决了设备相关难题，应对了安全挑战，并剖析了隐私问题的复杂性，同时强调了其在医疗领域的变革潜力。本研究主要聚焦于联邦学习在医疗应用中的实践，特别是全球癌症诊断领域。我们强调了联邦学习在使计算机辅助诊断工具比传统数据驱动方法更有效地应对这一挑战方面的潜力。希望这篇综合性综述能作为该领域的里程碑，总结当前最新技术，识别待解决问题并指出未来研究方向。