Client-Edge-Cloud Hierarchical Federated Learning
Open accessAbstract
Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients’ private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.
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Liu et al. proposed a client-edge-cloud hierarchical aggregation framework as a communication resource-efficient method to operate the federated learning in edge computing.
“Liu et al. also proposed a client-edge-cloud hierarchical aggregation framework as a communication resource-efficient method to operate the federated learning in edge computing [9].”
From Federated learning for the Internet of Things: Applications, challenges, and opportunities (2022) · cites this paper as [9]
In the client-edge-cloud paradigm, each client is able to offload its data samples and learning tasks from its device to the edge in proximity for fast computation, which allows multiple edge servers to perform partial model aggregation.
“Each client is able to offload its data samples and learning tasks from its device to the edge in proximity (e.g., edge gateway at home) for fast computation in the client-edge-cloud paradigm, which allows multiple edge servers to perform partial model aggregation.”
From Federated learning for the Internet of Things: Applications, challenges, and opportunities (2022) · cites this paper as [9]
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- Authors
- Liu, L., Zhang, J., Song, S. H., & Letaief, K. B.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Hong Kong Polytechnic University (HK)
- Venue
- ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1-6
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W3045638580
- Cited by
- 1,033
- References
- 10
- Citations by year
- 2026: 113 · 2025: 217 · 2024: 220 · 2023: 199 · 2022: 165 · 2021: 83 · 2020: 36
- FWCI
- 45.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Conference on Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (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 › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, hierarchical federated learning, edge servers, computation-communication trade-off, convergence analysis, data distribution, privacy-preserving machine learning
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