Federated Learning with Authenticated Clients

Data exhibit the distribution of the problem space, and the efficacy of machine learning models is contingent upon the availability of quality datasets. Additionally, in traditional machine learning models, data are required to be collected on a centralized server for training and testing, which raises privacy and security concerns. Federated Learning (FL) was designed with the aim of moving the computation to the data source (clients) instead of bringing the data to the central server. While addressing data accessibility and privacy issues, the eligibility of clients and the quality of their data should be considered in a secure FL framework. It is difficult to verify the authenticity of the clients participating in the FL environment. Various solutions have been proposed, including statistical analysis of client updates, hardware-based isolation, Differential Privacy (DP), Homomorphic Encryption (HE), and others. However, these solutions still pose limitations and suffer significantly from trade-offs, such as the privacy-utility tradeoff. In this research, we propose an approach to fortify the FL environment with continuous verification of client updates to prevent model poisoning attacks and use a filter ensemble to detect the data poisoning attacks. Our empirical experiments demonstrated improved performance against these attacks and alleviated the limitations in existing solutions.