Federated Learning in Data Privacy and Security

Abstract

Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security, quality and effective learning. This paper focuses on algorithms such as, federated averaging algorithm, differential privacy, federated stochastic variance and reduced gradient (FSVRG). To achieve data privacy and security, this research paper presents the main data statistics with the help of graphs, visual images and design models. Later, data security in federated learning models is researched and case studies are presented to identify risks and possible solutions. Detecting security gaps is a challenge for many companies. This paper presents solutions for the identification of security-related issues which results in a decrease in time complexity and an increase in accuracy. This research sheds light on the topics of federated learning and data security.

Keywords

• fl
• federated averaging
• differential privacy
• fsvrg