Securing federated learning: a defense mechanism against model poisoning threats

Abstract

Distributed machine learning advancements have the potential to transform future networking systems and communications. An effective framework for machine learning has been made possible by the introduction of Federated Learning (FL) and due to its decentralized nature it has some poisoning issues. Model poisoning attacks are one of them that significantly affect FL's performance. Model poisoning mainly defines the replacement of a functional model with a poisoned model by injecting poison into models in the training period. The model's boundary typically alters in some way as a result of a poisoning attack, which leads to unpredictability in the model outputs. Federated learning provides a mechanism to unleash data to fuel new AI applications by training AI models without access anyone's confidential data. Currently, there are many algorithms that are being used for defending model poisoning in federated learning. Some of them are really efficient but most of them have lots of issues that don't make the federated learning system properly secured. So in this study, we have highlighted the main issues of these algorithms and provided a defense mechanism that is capable of defending model poisoning in federated learning.