Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection

TL;DR

This paper introduces SBRO-FL, a unified framework for client selection in federated learning that balances data quality, incentives, and costs to improve model performance and robustness.

Contribution

It proposes a novel integrated approach combining dynamic bidding, reputation modeling, and cost-aware selection for federated learning.

Findings

Improves accuracy and convergence speed in FL.

Enhances robustness against adversarial clients.

Balances data quality and cost effectively.

Abstract

In cross-silo Federated Learning (FL), client selection is critical to ensure high model performance, yet it remains challenging due to data quality decompensation, budget constraints, and incentive compatibility. As training progresses, these factors exacerbate client heterogeneity and degrade global performance. Most existing approaches treat these challenges in isolation, making jointly optimizing multiple factors difficult. To address this, we propose Shapley-Bid Reputation Optimized Federated Learning (SBRO-FL), a unified framework integrating dynamic bidding, reputation modeling, and cost-aware selection. Clients submit bids based on their perceived data quality, and their contributions are evaluated using Shapley values to quantify their marginal impact on the global model. A reputation system, inspired by prospect theory, captures historical performance while penalizing inconsistency. The client selection problem is formulated as a 0-1 integer program that maximizes reputation-weighted utility under budget constraints. Experiments on FashionMNIST, EMNIST, CIFAR-10, and SVHN datasets show that SBRO-FL improves accuracy, convergence speed, and robustness, even in adversarial and low-bid interference scenarios. Our results highlight the importance of balancing data reliability, incentive compatibility, and cost efficiency to enable scalable and trustworthy FL deployments.