Energy-Efficient Radio Resource Allocation for Federated Edge Learning
Open accessAbstract
Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation. FEEL coordinates global model training at a server and local model training at edge devices over wireless links. In this work, we explore the new direction of energy-efficient radio resource management (RRM) for FEEL. To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling. They adapt to devices' channel states and computation capacities so as to reduce their sum energy consumption while warranting learning performance. In contrast with the traditional rate-maximization designs, the derived optimal policies allocate more bandwidth to those scheduled devices with weaker channels or poorer computation capacities, which are the bottlenecks of synchronized model updates in FEEL. On the other hand, the scheduling priority function derived in closed form gives preferences to devices with better channels and computation capacities. Substantial energy reduction contributed by the proposed strategies is demonstrated in learning experiments.
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Integration of an FL-based AI model can result in a more energy-efficient solution.
“Integration of an FL-based AI model can result in a more energy-efficient solution [177, 178].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [178]
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“One branch of research focuses on edge-device selection so as to accelerate learning [5], [6].”
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Federated Learning in Robotic and Autonomous Systems
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- Authors
- Zeng, Q., Du, Y., Huang, K., & Leung, K. K.
- Affiliations
- University of Hong Kong (HK)
- Imperial College London (GB)
- Venue
- 2020 IEEE International Conference on Communications Workshops (ICC Workshops)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1-6
- ISSN
- 2164-7038 (ISSN-L)
- OpenAlex
- W3045027907
- Cited by
- 286
- References
- 8
- Citations by year
- 2026: 11 · 2025: 25 · 2024: 40 · 2023: 53 · 2022: 55 · 2021: 65 · 2020: 34 · 2019: 3
- FWCI
- 43.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Communications workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- federated edge learning, radio resource management, energy efficiency, bandwidth allocation, device scheduling, channel state adaptation, data privacy, wireless edge computing
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