Y. Siriwardhana, P. Porambage, M. Liyanage and M. Ylianttila, "Robust and Resilient Federated Learning for Securing Future Networks," 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Grenoble, France, 2022, pp. 351-356, doi: 10.1109/EuCNC/6GSummit54941.2022.9815812
Robust and resilient federated learning for securing future networks
|Author:||Siriwardhana, Yushan1; Porambage, Pawani1; Liyanage, Madhusanka1,2;|
1Centre for Wireless Communications, University of Oulu, Finland
2School of Computer Science, University College Dublin, Ireland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301162892
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-01-16
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 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.
European Conference on Networks and Communications
|Pages:||351 - 356|
2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Joint European Conference on Networks and Communications & 6G Summit
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported by Academy of Finland in 6Genesis Flagship (grant no. 318927) project. The research leading to these results partly received funding from European Union’s Horizon 2020 research and innovation programme under grant agreement no 871808 (5G PPP project INSPIRE5Gplus) and 101021808 (H2020 SPATIAL project).
|EU Grant Number:||
(871808) INSPIRE-5Gplus - INtelligent Security and PervasIve tRust for 5G and Beyond
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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