Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach

TL;DR

This paper introduces a truthful auction-based long-term client selection method for federated learning that effectively handles non-IID data and resource constraints in IoV scenarios, improving model performance.

Contribution

It proposes a novel long-term client selection scheme using an auction mechanism that incentivizes truthful participation and considers energy costs, addressing non-IID data challenges.

Findings

The proposed scheme maximizes social welfare in federated learning.

It ensures incentive compatibility and individual rationality.

Experimental results show improved model accuracy under non-IID data.

Abstract

Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Internet of Vehicles (IoV). Each smart vehicle acts as a mobile client, contributing to the process without uploading local data. This method leverages non-independent and identically distributed (non-IID) training data from different vehicles, influenced by various driving patterns and environmental conditions, which can significantly impact model convergence and accuracy. Although client selection can be a feasible solution for non-IID issues, it faces challenges related to selection metrics. Traditional metrics evaluate client data quality independently per round and require client selection after all clients complete local training, leading to resource wastage from unused training results. In the IoV context, where vehicles have limited connectivity and computational resources, information asymmetry in client selection risks clients submitting false information, potentially making the selection ineffective. To tackle these challenges, we propose a novel Long-term Client-Selection Federated Learning based on Truthful Auction (LCSFLA). This scheme maximizes social welfare with consideration of long-term data quality using a new assessment mechanism and energy costs, and the advised auction mechanism with a deposit requirement incentivizes client participation and ensures information truthfulness. We theoretically prove the incentive compatibility and individual rationality of the advised incentive mechanism. Experimental results on various datasets, including those from IoV scenarios, demonstrate its effectiveness in mitigating performance degradation caused by non-IID data.