Toward Smart Security Enhancement of Federated Learning Networks
Open accessAbstract
As traditional centralized learning networks (CLNs) are facing increasing challenges in terms of privacy preservation, communication overheads, and scalability, federated learning networks (FLNs) have been proposed as a promising alternative paradigm to support the training of machine learning (ML) models. In contrast to the centralized data storage and processing in CLNs, FLNs exploit a number of edge devices (EDs) to store data and perform training distributively. In this way, the EDs in FLNs can keep training data locally, which preserves privacy and reduces communication overheads. However, since the model training within FLNs relies on the contribution of all EDs, the training process can be disrupted if some of the EDs upload incorrect or falsified training results, that is, poisoning attacks. In this article, we review the vulnerabilities of FLNs, and particularly give an overview of poisoning attacks and mainstream countermeasures. Nevertheless, the existing countermeasures can only provide passive protection and fail to consider the training fees paid for the contributions of the EDs, resulting in an unnecessarily high training cost. Hence, we present a smart security enhancement framework for FLNs. In particular, a verify-before-aggregate (VBA) procedure is developed to identify and remove the non-benign training results from the EDs. Afterward, deep reinforcement learning (DRL) is applied to learn the behaving patterns of the EDs and to actively select the EDs that can provide benign training results and charge low training fees. Simulation results reveal that the proposed framework can protect FLNs effectively and efficiently.
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A machine learning model can be faked when only a couple of edge devices are operating incorrectly.
“a machine learning model can be faked when only a couple of edge devices are operating incorrectly [137].”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [137]
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- Authors
- Tan, J., Liang, Y.-C., Luong, N. C., & Niyato, D.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Phenikaa University (VN)
- Nanyang Technological University (SG)
- Venue
- IEEE Network
- Published
- 2021-01
- Volume
- 35
- Issue
- 1
- Pages
- 340-347
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W3096718731
- Cited by
- 42
- References
- 16
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 10 · 2023: 10 · 2022: 6 · 2021: 7
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Network (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61631005,U1801261)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- federated learning security, poisoning attacks, deep reinforcement learning, edge devices, malicious device detection, privacy preservation, communication cost reduction
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