Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.

翻译：联邦学习（Federated Learning, FL）已成为人工智能（Artificial Intelligence, AI）领域的一项重大进展，它使得跨分布式设备的协作模型训练成为可能，同时保护了数据隐私。随着FL重要性的日益提升，解决其各个方面的可信性问题变得至关重要。在本综述中，我们全面概述了当前可信FL的研究现状，探讨了现有解决方案及与可信FL相关的明确定义的支柱。尽管关于可信中心化机器学习（Machine Learning, ML）/深度学习（Deep Learning, DL）的文献有所增长，但仍需进一步努力来识别FL模型特有的可信支柱和评估指标，并开发计算可信度水平的解决方案。我们提出一种分类体系，涵盖三大支柱：可解释性（Interpretability）、公平性（Fairness）以及安全与隐私（Security & Privacy）。每个支柱代表信任的一个维度，并进一步细分为不同的概念。我们的综述涵盖了FL设置中各个层面的可信挑战。我们呈现了可信FL的全面架构，阐述了其概念背后的基本原则，并对信任评估机制进行了深入分析。最后，我们指出了与可信FL每个方面相关的关键研究挑战，并提出了未来研究方向。这份全面的综述为从事可信FL系统开发与实现的研究人员和实践者提供了宝贵资源，有助于构建更安全、更可靠的人工智能格局。