A Comprehensive Analysis of Inference Attacks in Federated Learning

In the face of increasing data silos and mounting privacy concerns, the traditional centralized method of training machine learning models encounters significant obstacles. Federated Learning has emerged as a compelling solution in this evolving environment. However, the existing Federated Learning protocols have demonstrated vulnerabilities that adversaries can exploit, jeopardizing data privacy. This paper investigates the implications of various attacks on federated learning. We explore the impact of these attacks, considering the decentralized nature of Federated Learning and its susceptibility to privacy breaches. Additionally, we offer detailed implementations of the inference attacks and propose strategies to mitigate the potential risks, thus enhancing the overall privacy and security of the Federated Learning framework.