Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization
Kevin Cooper, Michael Geller
TL;DR
This work addresses the need for privacy-conscious personalization in distributed learning by integrating adaptive optimization, transfer learning, and differential privacy into Personalized Federated Learning (PFL). It introduces a Meta-Federated Learning framework that incorporates Model-Agnostic Meta-Learning (MAML) for rapid client adaptation and a dynamic control system to optimize convergence and personalization. Empirical results in a simulated water-management scenario show superior model accuracy, faster response times, and higher throughput compared with centralized ML and standard FL, underscoring improvements in both privacy and scalability. The study provides deployment guidelines for large-scale, real-time, privacy-preserving PFL across heterogeneous edge devices, with implications for regulated industries requiring stringent data protection.
Abstract
In the age of data-driven decision making, preserving privacy while providing personalized experiences has become paramount. Personalized Federated Learning (PFL) offers a promising framework by decentralizing the learning process, thus ensuring data privacy and reducing reliance on centralized data repositories. However, the integration of advanced Artificial Intelligence (AI) techniques within PFL remains underexplored. This paper proposes a novel approach that enhances PFL with cutting-edge AI methodologies including adaptive optimization, transfer learning, and differential privacy. We present a model that not only boosts the performance of individual client models but also ensures robust privacy-preserving mechanisms and efficient resource utilization across heterogeneous networks. Empirical results demonstrate significant improvements in model accuracy and personalization, along with stringent privacy adherence, as compared to conventional federated learning models. This work paves the way for a new era of truly personalized and privacy-conscious AI systems, offering significant implications for industries requiring compliance with stringent data protection regulations.