A robust analysis of adversarial attacks on federated learning environments

Federated Learning is a growing branch of Artificial Intelligence with the wide usage of mobile computing and IoT technologies. Since this technology uses distributed computing paradigm to do the learning part, most of the participating components are mobile devices and come outside the range of protection offered by a centralized system. As a result, several security issues such as data leakage, communication issues, poisoning, system manipulation via the backdoor, and so on arise with the usage of such a methodology. These sorts of attacks are categorized into various categories concerning their modus operandi. In this study, we review such attacks, namely poisoning attacks, inferencing attacks, their types, and working in a Federated Learning environment in detail. This study will give a precise idea of security issues faced in Federated Machine Learning and possible solutions.