Federated Learning for Privacy-Preserving Machine Learning

Abstract

Federated learning is a cutting-edge approach that revolutionizes machine learning by allowing multiple decentralized devices to collaboratively train a global model while keeping their data localized and private. This paper explores the crucial role of federated learning in achieving privacy-preserving machine learning, ensuring data security, and enabling collaborative model training across distributed edge devices. We delve into the technical aspects of federated learning, including its algorithms and architectures, and discuss its application in various domains, such as healthcare, finance, and IoT. Additionally, this paper highlights the challenges and potential solutions in federated learning concerning privacy, security, and model aggregation. The study demonstrates how federated learning addresses the trade-off between model performance and data privacy, making it a promising technique for the future of machine learning.