The DOI matches its Crossref record; 14 of the 16 references it cites were found in a registry or an index.
Device Scheduling with Fast Convergence for Wireless Federated Learning
Open accessAbstract
Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training framework called federated learning (FL) has emerged. In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels. Due to the limited spectrum resources, only a portion of the devices can be scheduled in each round. While most of the existing work on scheduling focuses on the convergence of FL w.r.t. rounds, the convergence performance under a total training time budget is not yet explored. In this paper, a joint bandwidth allocation and scheduling problem is formulated to capture the long-term convergence performance of FL, and is solved by being decoupled into two sub-problems. For the bandwidth allocation sub-problem, the derived optimal solution suggests to allocate more bandwidth to the devices with worse channel conditions or weaker computation capabilities. For the device scheduling sub-problem, by revealing the trade-off between the number of rounds required to attain a certain model accuracy and the latency per round, a greedy policy is inspired, that continuously selects the device that consumes the least time in model updating until achieving a good trade-off between the learning efficiency and latency per round. The experiments show that the proposed policy outperforms other state-of-the-art scheduling policies, with the best achievable model accuracy under training time budgets.
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Metadata
- Authors
- Shi, W., Zhou, S., & Niu, Z.
- Affiliations
- Beijing National Research Center for Information Science and Technology (CN)
- Venue
- ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1-6
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W3046056026
- Cited by
- 216
- References
- 16
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 35 · 2023: 44 · 2022: 52 · 2021: 45 · 2020: 17
- FWCI
- 14.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Communications (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 › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- wireless federated learning, device scheduling, bandwidth allocation, edge computing, model updating, channel conditions
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