Privacy Preserving and Federated Learning In Distributed Analytics: Enhancing Data Security And Collaborative Intelligence

Abstract

In the era of big data, distributed analytics has emerged as a vital paradigm for processing large volumes of data across multiple nodes and devices. However, conventional centralized learning models often face significant privacy concerns, especially when sensitive data is involved. Privacy-preserving techniques combined with federated learning provide an effective solution to mitigate data leakage while enabling collaborative intelligence. This paper explores the principles of privacypreserving federated learning, its architecture, methodologies, challenges, and potential applications. By integrating cryptographic techniques, secure aggregation, and decentralized learning models, organizations can achieve efficient analytics while maintaining data confidentiality. This work provides an extensive analysis of the state-of-the-art approaches, identifies existing gaps, and presents insights into future research directions in privacy-preserving distributed analytics

KEYWORDS: Federated Learning, Privacy-Preserving Analytics, Distributed Systems, Secure Aggregation, Data Confidentiality, Collaborative Machine Learning, Decentralized AI