Accelerating Federated Learning with genetic algorithm enhancements

Abstract

Federated Learning (FL) enables collaborative model training across multiple devices while preserving data privacy. However, developing robust and efficient FL faces significant challenges, such as data heterogeneity, computational resource constraints, communication bottlenecks, and the presence of malicious participants. To address these issues, we introduce GenFed, an innovative framework that enhances federated learning through genetic algorithm mechanisms. GenFed optimizes model aggregation strategies and balances resource utilization, thereby improving performance and resilience. This framework is designed for seamless integration with existing FL systems, facilitating rapid adaptation. GenFed accelerates model convergence and enhances robustness, particularly in environments with a large number of clients. Experimental results demonstrate that GenFed significantly outperforms traditional FL methods in terms of convergence speed, accuracy, and resilience against adversarial attacks across diverse datasets. Notably, as the number of clients increases, conventional federated methods often suffer substantial performance degradation. In contrast, GenFed maintains stable, high-level performance, making it especially practical for real-world scenarios involving extensive client participation. Our findings indicate that GenFed is a versatile and efficient solution that offers significant improvements in scalability and robustness, contributing to the deployment of reliable federated learning in real-world applications.