An approach utilizing frequency analysis to counteract poisoning attacks in Federated Learning

Abstract

Machine learning requires diverse training datasets from multiple clients for improved performance. However, sharing datasets is often a legal and privacy issue across countries and organizations. Federated Learning (FL) is a machine learning framework allowing individual clients to train datasets locally and share only the weight updates to a central server where the updates are aggregated. FL addresses the security and privacy issues concerned with data-sharing; however, it is vulnerable to poisoning attacks where a malicious client can purposefully alter the model updates. Even a smallest input deviation can exploit the system leading to misclassification. In this study, we propose a lightweight defense mechanism for mitigating poisoning attacks in Federated Learning (FL) systems. Our approach involves transforming model weights into the frequency domain to identify core frequency components containing sufficient model weight information. Additionally, we employ a model filtering algorithm to predict poisoning attacks based on the output of the frequency analysis method. This enables effective filtering of malicious updates during local training on client devices. Our proposed defense mechanism enhances the security and integrity of FL systems against adversarial attack ensuring secure model aggregation.