The rapid expansion of the Internet of Things (IoT) and Edge Computing has presented challenges for centralized Machine and Deep Learning (ML/DL) methods due to the presence of distributed data silos that hold sensitive information. To address concerns regarding data privacy, collaborative and privacy-preserving ML/DL techniques like Federated Learning (FL) have emerged. However, ensuring data privacy and performance alone is insufficient since there is a growing need to establish trust in model predictions. Existing literature has proposed various approaches on trustworthy ML/DL (excluding data privacy), identifying robustness, fairness, explainability, and accountability as important pillars. Nevertheless, further research is required to identify trustworthiness pillars and evaluation metrics specifically relevant to FL models, as well as to develop solutions that can compute the trustworthiness level of FL models. This work examines the existing requirements for evaluating trustworthiness in FL and introduces a comprehensive taxonomy consisting of six pillars (privacy, robustness, fairness, explainability, accountability, and federation), along with over 30 metrics for computing the trustworthiness of FL models. Subsequently, an algorithm named FederatedTrust is designed based on the pillars and metrics identified in the taxonomy to compute the trustworthiness score of FL models. A prototype of FederatedTrust is implemented and integrated into the learning process of FederatedScope, a well-established FL framework. Finally, five experiments are conducted using different configurations of FederatedScope to demonstrate the utility of FederatedTrust in computing the trustworthiness of FL models. Three experiments employ the FEMNIST dataset, and two utilize the N-BaIoT dataset considering a real-world IoT security use case.

翻译：物联网和边缘计算的快速发展，使得集中式机器学习和深度学习方法面临挑战，原因在于存在持有敏感信息的分布式数据孤岛。为解决数据隐私问题，联邦学习等协作式且保护隐私的机器学习/深度学习方法应运而生。然而，仅确保数据隐私和性能是不够的，因为人们越来越需要在模型预测中建立信任。现有文献提出了多种可信机器学习/深度学习方法（不包括数据隐私），将鲁棒性、公平性、可解释性和问责性确定为重要支柱。尽管如此，仍需进一步研究，以确定与联邦学习模型特别相关的可信支柱和评估指标，并开发能够计算联邦学习模型可信度水平的解决方案。本文审视了联邦学习中评估可信度的现有要求，并引入了一个由六项支柱（隐私、鲁棒性、公平性、可解释性、问责性和联邦性）及30多个指标组成的综合分类体系，用于计算联邦学习模型的可信度。随后，基于该分类体系中的支柱和指标设计了一种名为FederatedTrust的算法，用于计算联邦学习模型的可信度评分。我们实现了FederatedTrust原型，并将其整合到成熟联邦学习框架FederatedScope的学习过程中。最后，使用FederatedScope的不同配置进行了五项实验，以展示FederatedTrust在计算联邦学习模型可信度方面的实用性。其中三项实验采用FEMNIST数据集，另外两项则基于现实物联网安全用例使用N-BaIoT数据集。