Federated Learning for Privacy-Preserving Data Analysis
Abstract
This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model training without direct data sharing. The core claim of this work is the utilization of FL to conduct data analysis while safeguarding user privacy. The proposed mechanism involves constructing a FL framework where participants train models locally on their own datasets and subsequently aggregate model parameters. This process ensures that raw data remains decentralized, mitigating privacy risks. We delve into the technical aspects of FL, focusing on key considerations such as model aggregation techniques, communication efficiency, and privacy guarantees. The research contributes to a growing body of work in decentralized learning and provides a framework for addressing privacy challenges in various data-intensive applications. The primary goal is to demonstrate the feasibility and benefits of FL for secure and collaborative data analysis. ---
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