2024/09/15 by Hao Huang, Huang, Hao Jian, Hakan T. Otal +3 · 1 citation
Computer Science · #68M14 #68M15 #68T05 #Adversarial Robustness in Machine Learning #C.2.4 #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #I.2.11 #I.2.6 #K.6.5 #Machine Learning (cs.LG) #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2409.09794
openalex publication_date 2024/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents the design and implementation of a Federated Learning (FL) testbed, focusing on its application in cybersecurity and evaluating its resilience against poisoning attacks. Federated Learning allows multiple clients to collaboratively train a global model while keeping their data decentralized, addressing critical needs for data privacy and security, particularly in sensitive fields like cybersecurity. Our testbed, built using Raspberry Pi and Nvidia Jetson hardware by running the Flower framework, facilitates experimentation with various FL frameworks, assessing their performance, scalability, and ease of integration. Through a case study on federated intrusion detection systems, the testbed's capabilities are shown in detecting anomalies and securing critical infrastructure without exposing sensitive network data. Comprehensive poisoning tests, targeting both model and data integrity, evaluate the system's robustness under adversarial conditions. The results show that while federated learning enhances data privacy and distributed learning, it remains vulnerable to poisoning attacks, which must be mitigated to ensure its reliability in real-world applications.