Abstract

Federated Learning (FL) is a machine learning paradigm that enables model training across decentralized devices while preserving data privacy. However, FL faces two significant challenges: privacy concerns and scalability issues. Privacy concerns arise from potential vulnerabilities in aggregating updates, whereas scalability issues stem from the increasing number of edge devices and the computational overhead required for communication and model updates. This paper explores cutting-edge advancements aimed at addressing these challenges, including advanced encryption techniques, differential privacy mechanisms, federated optimization methods, and decentralized training architectures. We also discuss strategies for managing communication costs, improving convergence speeds, and ensuring robustness in heterogeneous environments. By integrating novel approaches to privacy and scalability, next-generation federated learning can provide a more secure, efficient, and scalable framework for a wide range of applications, from healthcare to autonomous vehicles.