Federated Learning for Distributed Cloud AI Models: A Comprehensive Study on Privacy-Preserving Training across Distributed Systems

Abstract

With the exponential rise of cloud-native applications, AI model training across distributed cloud environments presents both opportunities and challenges. Traditional centralized training approaches raise concerns regarding data privacy, communication overhead, and regulatory compliance. Federated Learning (FL) emerges as a promising solution by enabling decentralized training across multiple cloud systems without requiring raw data aggregation. This paper investigates the application of federated learning techniques for distributed cloud AI models, proposing an enhanced privacy-preserving framework adapted for heterogeneous environments. Detailed implementation, experimental validation, performance evaluation, and critical discussions are presented, offering deep insights into real-world deployment considerations.