Decentralized Wireless Federated Learning With Differential Privacy
Open accessAbstract
This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a decentralized wireless federated learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies$(\epsilon,\delta)$-differential privacy and the privacy budget per worker scales as$\mathcal {O}(\frac{1}{\sqrt{N}})$, in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of$\mathcal {O}(\sqrt{\frac{1}{TN}})$as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.
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In decentralized WFL (DWFL), there is an increased risk of private information leakage due to the transmission of model parameters over a wireless network.
“In [95], decentralized WFL (DWFL) is studied, where there is an increased risk of private information leakage due to the transmission of model parameters over a wireless network.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [95]
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- Authors
- Chen, S., Yu, D., Zou, Y., Yu, J., & Cheng, X.
- Affiliations
- Shandong University (CN)
- Qilu University of Technology (CN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-09
- Volume
- 18
- Issue
- 9
- Pages
- 6273-6282
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4226126236
- Cited by
- 100
- References
- 21
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 32 · 2023: 20 · 2022: 5
- FWCI
- 7.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Key R&D Program of China (2019YFB2102600)
- National Natural Science Foundation of China (62122042, 61832012)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- decentralized federated learning, wireless federated learning, differential privacy, wireless IoT networks, peer learning, serverless architecture, analog signal transmission, privacy budget, convergence rate
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