Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation

2025-03-29

AI Summary

In federated learning, client data heterogeneity degrades model performance, impairs convergence stability, and exacerbates privacy risks. To address these challenges, we propose ClusterGuardFL—a dynamic weighted aggregation framework. Its core contributions are threefold: (1) adaptive clustering based on a dissimilarity score to identify semantically similar clients; (2) cluster-size-aware weighting to mitigate bias from small clusters; and (3) point-level reconciliation guided by confidence-aware softmax weights, jointly enhancing robustness, fairness, and differential privacy compatibility. The framework integrates k-means clustering, model dissimilarity measurement, confidence modeling, and secure aggregation. Extensive experiments on multi-source heterogeneous datasets demonstrate that ClusterGuardFL significantly improves global model accuracy and convergence stability, effectively suppressing interference from malicious or low-quality clients. Results validate the synergistic benefits of its weighted aggregation strategy in simultaneously strengthening robustness and privacy preservation.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Large-scale security measurements

Abstract

Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sharing. In FL, a global model is trained iteratively on local datasets residing on individual devices, each contributing to the model's improvement. However, the heterogeneous nature of these local datasets, stemming from diverse user behaviours, device capabilities, and data distributions, poses a significant challenge. The inherent heterogeneity in federated learning gives rise to various issues, including model performance discrepancies, convergence challenges, and potential privacy concerns. As the global model progresses through rounds of training, the disparities in local data quality and quantity can impede the overall effectiveness of federated learning systems. Moreover, maintaining fairness and privacy across diverse user groups becomes a paramount concern. To address this issue, this paper introduces a novel FL framework, ClusterGuardFL, that employs dissimilarity scores, k-means clustering, and reconciliation confidence scores to dynamically assign weights to client updates. The dissimilarity scores between global and local models guide the formation of clusters, with cluster size influencing the weight allocation. Within each cluster, a reconciliation confidence score is calculated for individual data points, and a softmax layer generates customized weights for clients. These weights are utilized in the aggregation process, enhancing the model's robustness and privacy. Experimental results demonstrate the efficacy of the proposed approach in achieving improved model performance in diverse datasets.

Problem

• Addressing data heterogeneity in federated learning systems
• Improving model performance and convergence in FL
• Ensuring privacy and fairness across diverse user groups

Innovation

• ClusterGuardFL uses dissimilarity scores for clustering
• Dynamic weight allocation via reconciliation confidence scores
• Softmax layer generates customized client weights