Integrating Personalized Federated Learning with Control Systems for Enhanced Performance

Abstract

In the expanding field of machine learning, federated learning has emerged as a pivotal methodology for distributed data environments, ensuring privacy while leveraging decentralized data sources. However, the heterogeneity of client data and the need for tailored models necessitate the integration of personalization techniques to enhance learning efficacy and model performance. This paper introduces a novel framework that amalgamates personalized federated learning with robust control systems, aimed at optimizing both the learning process and the control of data flow across diverse networked environments. Our approach harnesses personalized algorithms that adapt to the unique characteristics of each client's data, thereby improving the relevance and accuracy of the model for individual nodes without compromising the overall system performance. To manage and control the learning process across the network, we employ a sophisticated control system that dynamically adjusts the parameters based on real-time feedback and system states, ensuring stability and efficiency. Through rigorous experimentation, we demonstrate that our integrated system not only outperforms standard federated learning models in terms of accuracy and learning speed but also maintains system integrity and robustness in face of varying network conditions and data distributions. The experimental results, obtained from a multi-client simulated environment with non-IID data distributions, underscore the benefits of integrating control systems into personalized federated learning frameworks, particularly in scenarios demanding high reliability and precision.