Abstract

Federated learning frameworks offer a promising approach to distributed big data analytics in cloud computing environments, enabling collaborative model training across decentralized data sources while preserving data privacy and security. This paper presents an overview of federated learning frameworks tailored for distributed big data analytics in cloud computing settings. We examine the fundamental principles of federated learning, including decentralized model training, gradient aggregation, and model synchronization, within the context of cloud computing infrastructure. Additionally, we review prominent federated learning frameworks and architectures designed to address scalability, efficiency, and privacy concerns in large-scale distributed settings. Through a comparative analysis of these frameworks, we highlight their key features, performance characteristics, and applicability to various use cases in cloud-based big data analytics. Furthermore, we discuss emerging trends and challenges in federated learning for distributed big data analytics and propose future research directions to advance the state-of-the-art in this field. By synthesizing existing literature and practical insights, this study aims to provide guidance for researchers and practitioners seeking to leverage federated learning for efficient and privacy-preserving big data analytics in cloud computing environments.

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