Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities

TL;DR

This paper introduces a mutual trust framework for federated learning in smart cities, enhancing trustworthiness of clients and servers, improving model accuracy, and reducing malicious participants through reputation and preference-based mechanisms.

Contribution

It proposes a novel mutual trust framework that considers trust needs of both clients and servers, incorporating reputation systems and intelligent matching algorithms in federated learning.

Findings

Outperforms baseline methods in trust levels and model accuracy

Reduces the presence of non-trustworthy clients

Enhances system integrity through reputation-based assessments

Abstract

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.