Empowering Privacy-Preserving Machine Learning: A Comprehensive Survey on Federated Learning
I.V. Dwaraka Srihith, A. David Donald, T. Aditya Sai Srinivas, G. Thippanna, D. Anjali · International Journal of Advanced Research in Science Communication and Technology · 2023
As the need for machine learning models continues to grow, concerns about data privacy and security become increasingly important. Federated learning, a decentralized machine learning approach, has emerged as a promising solution that allows multiple parties to collaborate and build models without sharing sensitive data. In this comprehensive survey, we explore the principles, techniques, and applications of federated learning, with a focus on its privacy-preserving aspects