Federated Learning in Cloud and Distributed Computing Environments: A Review Paper

Abstract

Federated learning has emerged as an important Artificial Intelligence paradigm that enables collaborative model training without centralized data sharing, thereby improving privacy and security in cloud and distributed computing systems. This review paper examines the development and implementation of federated learning frameworks across cloud computing, mobile networks, healthcare systems, and Internet of Things applications. The study reviews distributed optimization techniques, communication-efficient learning models, privacy-preserving algorithms, and decentralized AI architectures used in federated learning environments. Existing research is critically analyzed to identify key challenges including model heterogeneity, communication overhead, scalability, and data imbalance issues. The paper also discusses security threats such as poisoning attacks and privacy leakage risks in collaborative learning systems. The findings suggest that federated learning has strong potential for enabling secure, scalable, and privacy-aware AI services in future cloud ecosystems.