Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system's integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of trust in federated servers. To bridge this gap, in this work, we propose a novel framework that addresses the mutual trustworthiness in federated learning by considering the trust needs of both the client and the server. Our approach entails: (1) Creating preference functions for servers and clients, allowing them to rank each other based on trust scores, (2) Establishing a reputation-based recommendation system leveraging multiple clients to assess newly connected servers, (3) Assigning credibility scores to recommending devices for better server trustworthiness measurement, (4) Developing a trust assessment mechanism for smart devices using a statistical Interquartile Range (IQR) method, (5) Designing intelligent matching algorithms considering the preferences of both parties. Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and reducing non-trustworthy clients in the system.

翻译：联邦学习是一种在数据丰富的智慧城市中极具前景的协作式隐私保护机器学习方法。然而，这些城市环境固有的异质性给选择可信客户端进行协作模型训练带来了重大挑战。传统方法（如随机客户端选择技术）的使用可能因恶意客户端的选择而对系统完整性构成多重威胁。现有文献主要侧重于评估客户端的可信度，而忽视了联邦服务器端信任这一关键维度。为弥补这一空白，本文提出了一种新颖框架，通过同时考虑客户端与服务器的信任需求，解决联邦学习中的互信问题。我们的方法包括：（1）为服务器和客户端创建偏好函数，使其能够基于信任分数对彼此进行排序；（2）建立基于声誉的推荐系统，利用多个客户端评估新接入的服务器；（3）为推荐设备分配可信度分数，以更准确地衡量服务器的可信度；（4）利用统计四分位距法（IQR）开发智能设备的信任评估机制；（5）设计考虑双方偏好的智能匹配算法。基于仿真与实验结果表明，我们的方法在提升信任水平、全局模型准确率以及减少系统中非可信客户端数量方面优于基线方法。