  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.
