Visible to the public Robust and Resilient Federated Learning for Securing Future Networks

TitleRobust and Resilient Federated Learning for Securing Future Networks
Publication TypeConference Paper
Year of Publication2022
AuthorsSiriwardhana, Yushan, Porambage, Pawani, Liyanage, Madhusanka, Ylianttila, Mika
Conference Name2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Date Publishedjun
Keywords5G mobile communication, 6G mobile communication, AI Poisoning, Clustering algorithms, defense mechanism, Distributed databases, federated learning, Human Behavior, Industries, Label Flipping, machine learning, poisoning attacks, pubcrawl, resilience, Resiliency, Scalability, Training
AbstractMachine Learning (ML) and Artificial Intelligence (AI) techniques are widely adopted in the telecommunication industry, especially to automate beyond 5G networks. Federated Learning (FL) recently emerged as a distributed ML approach that enables localized model training to keep data decentralized to ensure data privacy. In this paper, we identify the applicability of FL for securing future networks and its limitations due to the vulnerability to poisoning attacks. First, we investigate the shortcomings of state-of-the-art security algorithms for FL and perform an attack to circumvent FoolsGold algorithm, which is known as one of the most promising defense techniques currently available. The attack is launched with the addition of intelligent noise at the poisonous model updates. Then we propose a more sophisticated defense strategy, a threshold-based clustering mechanism to complement FoolsGold. Moreover, we provide a comprehensive analysis of the impact of the attack scenario and the performance of the defense mechanism.
Citation Keysiriwardhana_robust_2022