Robust and Resilient Federated Learning for Securing Future Networks
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|Title:||Robust and Resilient Federated Learning for Securing Future Networks||Authors:||Siriwardhana, Yushan; Porambage, Pawani; Liyanage, Madhusanka; Ylianttila, Mika||Permanent link:||http://hdl.handle.net/10197/13091||Date:||10-Jun-2022||Online since:||2022-08-24T11:04:48Z||Abstract:||Machine 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 applicabil- ity 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.||Funding Details:||European Commission Horizon 2020||Type of material:||Conference Publication||Publisher:||IEEE||Start page:||351||End page:||356||Copyright (published version):||2022 IEEE||Keywords:||Federated learning; Poisoning attacks; Defense mechanism; Label flipping||DOI:||10.1109/eucnc/6gsummit54941.2022.9815812||Language:||en||Status of Item:||Peer reviewed||Is part of:||2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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