BACombo—Bandwidth-Aware Decentralized Federated Learning
Open accessAbstract
The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to efficiently utilize network capacities between computing nodes is crucial for conventional federated learning. In this paper, we propose Bandwidth Aware Combo (BACombo), a model segment level decentralized federated learning, to tackle this problem. In BACombo, we propose a segmented gossip aggregation mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, a bandwidth-aware worker selection model further reduces the transmission delay by greedily choosing the bandwidth-sufficient worker. The convergence guarantees are provided for BACombo. The experimental results on various datasets demonstrate that the training time is reduced by up to 18 times that of baselines without accuracy degrade.
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Decentralized training is more efficient than centralized training when running federated systems in low-bandwidth or high-latency networks.
“Decentralized training has also been shown to be more efficient than centralized training when running federated systems in low-bandwidth or high-latency networks (Xiao et al. 2020;Liu et al. 2020;Jiang et al. 2020).”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Jiang et al. 2020
The Introduction and Conclusions say BACombo is designed to avoid centralized-server congestion on poor network connections and report reduced training time. However, §6.1.3 lists only decentralized Gossip and Combo as experimental baselines; the paper does not demonstrate a training-time advantage over a centralized system or specifically test high-latency networks. It supports the low-bandwidth rationale, not the full comparative claim. (LLM, conf=0.86)
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A federated learning–driven data fusion strategy for the hardenability prediction of gear steel
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- Authors
- Jiang, J., Hu, L., Hu, C., Liu, J., & Wang, Z.
- Affiliations
- Jilin University (CN)
- Tsinghua Shenzhen International Graduate School (CN)
- Peng Cheng Laboratory (CN)
- Corresponding author
- Wang, Zhi
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2020-03-05
- Volume
- 9
- Issue
- 3
- Pages
- 440
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3009426715
- Cited by
- 47
- References
- 22
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 4 · 2023: 12 · 2022: 12 · 2021: 2 · 2020: 3
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Keywords
- decentralized federated learning, model segmentation, worker selection, network capacity, communication efficiency, convergence guarantees, data privacy
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