Reliable Federated Learning for Mobile Networks
Open accessAbstract
Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, for example, the data poisoning attack, or unintentionally, for example, low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.
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MSL scheme performs the same as ATV scheme when the reputation is higher than 0.35.
“MSL scheme performs the same as ATV scheme when the reputation is higher than 0.35.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [6]
ATV scheme under lower reputation thresholds provides higher accuracy than MSL.
“ATV scheme under lower reputation thresholds provides higher accuracy than MSL.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [6]
Reputation is introduced in the context of FL-enabled healthcare systems to prevent unreliable updates from untrusted devices.
“To prevent unreliable updates from untrusted devices, a new concept called reputation is introduced in the context of FL-enabled healthcare systems [51].”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [51]
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- Authors
- Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M.
- Affiliations
- Nanyang Technological University (SG)
- Huazhong University of Science and Technology (CN)
- Qatar University (QA)
- Venue
- IEEE Wireless Communications
- Published
- 2020-04
- Volume
- 27
- Issue
- 2
- Pages
- 72-80
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W3006403513
- Cited by
- 617
- References
- 16
- Citations by year
- 2026: 47 · 2025: 91 · 2024: 106 · 2023: 110 · 2022: 134 · 2021: 103 · 2020: 24 · 2019: 2
- FWCI
- 36.13
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Wireless Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61601336)
- Israel Science Foundation
- Ministry of Education, India
- Singapore University of Technology and Design (RGANS1906)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Keywords
- federated learning, worker reliability, worker selection, reputation management, consortium blockchain, data poisoning, cellular networks, privacy preservation
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