FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks

2025-09-24

AI Summary

To address fairness deficiencies and malicious participation risks in client selection for federated learning (FL) within heterogeneous Internet of Vehicles (IoV) environments, this paper proposes a fair, secure, and dynamically adaptive client selection framework. The framework innovatively integrates a sampling balancing module, a dynamic participation regulation mechanism, and a lightweight anomaly detection method based on model performance fluctuations (accuracy/loss). It is the first to achieve joint optimization of fairness, efficiency, and robustness in FL-based collaborative training under dynamic IoV conditions. Extensive experiments on the FEMNIST dataset demonstrate that the proposed method improves model accuracy by up to 2.3% over state-of-the-art baselines, reduces training standard deviation by 37%, and significantly enhances convergence stability and resilience against malicious attacks.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Adversarial Attacks & Robustness
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mitigating the communication overhead. Internet of Vehicles (IoV) is one of the promising applications, wherein FL can be utilized to train a model more efficiently. Since only a subset of the clients can participate in each FL training round, challenges arise pertinent to fairness in the client selection process. Over the years, a number of researchers from both academia and industry have proposed numerous FL frameworks. However, to the best of our knowledge, none of them have employed fairness for FL-based client selection in a dynamic and heterogeneous IoV environment. Accordingly, in this paper, we envisage a FairEquityFL framework to ensure an equitable opportunity for all the clients to participate in the FL training process. In particular, we have introduced a sampling equalizer module within the selector component for ensuring fairness in terms of fair collaboration opportunity for all the clients in the client selection process. The selector is additionally responsible for both monitoring and controlling the clients' participation in each FL training round. Moreover, an outlier detection mechanism is enforced for identifying malicious clients based on the model performance in terms of considerable fluctuation in either accuracy or loss minimization. The selector flags suspicious clients and temporarily suspend such clients from participating in the FL training process. We further evaluate the performance of FairEquityFL on a publicly available dataset, FEMNIST. Our simulation results depict that FairEquityFL outperforms baseline models to a considerable extent.

Problem

• Ensuring fair client selection in federated learning for IoV networks
• Addressing fairness challenges in dynamic heterogeneous vehicle environments
• Preventing malicious client participation through outlier detection mechanisms

Innovation

• Fair client selection using sampling equalizer module
• Monitoring and controlling client participation in rounds
• Outlier detection for malicious client identification