Federated Learning; Privacy Preserving Machine Learning for Decentralized Data
Abstract
Federated learning represents a compelling solution for tackling the privacy challenges inherent in decentralized and distributed environments when it comes to machine learning. This scholarly paper delves deep into the realm of federated learning, encompassing its applications and the latest privacy-preserving techniques used for training machine learning models in a decentralized manner. We explore the reasons behind the adoption of federated learning, highlight its advantages over conventional centralized approaches, and examine the diverse methods employed to safeguard privacy within this framework. Furthermore, we scrutinize the current obstacles, unresolved research queries, and the prospective directions within this rapidly developing field
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