Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning

• URL: http://arxiv.org/abs/2504.20346v1
• Date: Tue, 29 Apr 2025 01:31:23 GMT
• Title: Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning
• Authors: Chengui Xiao, Songbai Liu,
• Abstract summary: Federated learning enables decentralized model training while preserving data privacy.<n>It faces challenges in balancing communication efficiency, model performance, and privacy protection.<n>We propose FedMOEAC, a clustering-based evolutionary algorithm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning enables decentralized model training while preserving data privacy, yet it faces challenges in balancing communication efficiency, model performance, and privacy protection. To address these trade-offs, we formulate FL as a federated multiobjective optimization problem and propose FedMOEAC, a clustering-based evolutionary algorithm that efficiently navigates the Pareto-optimal solution space. Our approach integrates quantization, weight sparsification, and differential privacy to reduce communication overhead while ensuring model robustness and privacy. The clustering mechanism en-hances population diversity, preventing premature convergence and improving optimization efficiency. Experimental results on MNIST and CIFAR-10 demonstrate that FedMOEAC achieves 98.2% accuracy, reduces communication overhead by 45%, and maintains a privacy budget below 1.0, outperforming NSGA-II in convergence speed by 33%. This work provides a scalable and efficient FL framework, ensuring an optimal balance between accuracy, communication efficiency, and privacy in resource-constrained environments.

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