On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
Open accessAbstract
Most of the current literature focused on centralized learning is centered around the celebrated average-consensus paradigm and less attention is devoted to scenarios where the communication between the agents may be imperfect. This paper presents three different algorithms of Decentralized Federated Learning (DFL) in the presence of imperfect information sharing modeled as noisy communication channels. The first algorithm, Federated Noisy Decentralized Learning (FedNDL1) comes from the literature, where the noise is added to the algorithm parameters to simulate the scenario of the presence of noisy communication channels. This algorithm shares parameters to form a consensus with the clients based on a communication graph topology through a noisy communication channel. The proposed second algorithm (FedNDL2) is similar to the first algorithm but with added noise to the parameters and it performs the gossip averaging before the gradient optimization. The proposed third algorithm (FedNDL3), on the other hand, shares the gradients through noisy communication channels instead of the parameters. Theoretical and experimental results show that under imperfect information sharing, the third scheme that mixes gradients is more robust in the presence of a noisy channel compared with the algorithms from the literature that mix the parameters.
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Federated Learning and blockchain have emerged as promising solutions to address challenges of decentralized data-sharing.
“Several decentralized data-sharing technologies and techniques, such as Federated Learning (FL) [10] and blockchain [11], have emerged as promising solutions to address these challenges.”
From Federated learning meets blockchain in decentralized data-sharing: Healthcare use case (2024) · cites this paper as [10]
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- Authors
- Chellapandi, V. P., Upadhyay, A., Hashemi, A., & Żak, S. H.
- Affiliations
- Purdue University West Lafayette (US)
- College of Engineering, Purdue University, West Lafayette, IN, USA
- Venue
- IEEE Control Systems Letters
- Published
- 2023
- Volume
- 7
- Pages
- 2982-2987
- ISSN
- 2475-1456 (ISSN-L)
- OpenAlex
- W4382365395
- Cited by
- 38
- References
- 35
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 17 · 2023: 8
- FWCI
- 3.36
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Control Systems Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- decentralized federated learning, noisy channels, consensus algorithms, gradient averaging, parameter averaging, convergence analysis, communication topology, distributed optimization
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