Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks
Federated learning (FL) has recently received considerable attention and is becoming a popular machine learning (ML) framework that allows clients to train machine learning models in a decentralized fashion without sharing any private dataset. In the FL framework, data for learning tasks are acquire...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9924192/ |
| _version_ | 1797989529085280256 |
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| author | Tu Viet Nguyen Nhan Duc Ho Hieu Thien Hoang Cuong Danh Do Kok-Seng Wong |
| author_facet | Tu Viet Nguyen Nhan Duc Ho Hieu Thien Hoang Cuong Danh Do Kok-Seng Wong |
| author_sort | Tu Viet Nguyen |
| collection | DOAJ |
| description | Federated learning (FL) has recently received considerable attention and is becoming a popular machine learning (ML) framework that allows clients to train machine learning models in a decentralized fashion without sharing any private dataset. In the FL framework, data for learning tasks are acquired and processed locally at the edge node, and only the updated ML parameters are transmitted to the central server for aggregation. However, because local FL parameters and the global FL model are transmitted over wireless links, wireless network performance will affect FL training performance. In particular, the number of resource blocks is limited; thus, the number of devices participating in FL is limited. Furthermore, edge nodes often have substantial constraints on their resources, such as memory, computation power, communication, and energy, severely limiting their capability to train large models locally. This paper proposes a two-hop communication protocol with a dynamic resource allocation strategy to investigate the possibility of bandwidth allocation from a limited network resource to the maximum number of clients participating in FL. In particular, we utilize an ordinary hierarchical FL with an adaptive grouping mechanism to select participating clients and elect a leader for each group based on its capability to upload the aggregated parameters to the central server. Our experimental results demonstrate that the proposed solution outperforms the baseline algorithm in terms of communication cost and model accuracy. |
| first_indexed | 2024-04-11T08:20:47Z |
| format | Article |
| id | doaj.art-d4fff239d6e2460397a5fc61eb99bbe0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T08:20:47Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d4fff239d6e2460397a5fc61eb99bbe02022-12-22T04:34:58ZengIEEEIEEE Access2169-35362022-01-011011191011192210.1109/ACCESS.2022.32157589924192Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications NetworksTu Viet Nguyen0https://orcid.org/0000-0002-6459-4624Nhan Duc Ho1Hieu Thien Hoang2https://orcid.org/0000-0002-5435-8468Cuong Danh Do3Kok-Seng Wong4https://orcid.org/0000-0002-2029-7644Wireless Communications and Connectivity Division, Broadcom Ltd., San Diego, CA, USACollege of Engineering and Computer Science, VinUniversity, Hanoi, VietnamCollege of Engineering and Computer Science, VinUniversity, Hanoi, VietnamCollege of Engineering and Computer Science, VinUniversity, Hanoi, VietnamCollege of Engineering and Computer Science, VinUniversity, Hanoi, VietnamFederated learning (FL) has recently received considerable attention and is becoming a popular machine learning (ML) framework that allows clients to train machine learning models in a decentralized fashion without sharing any private dataset. In the FL framework, data for learning tasks are acquired and processed locally at the edge node, and only the updated ML parameters are transmitted to the central server for aggregation. However, because local FL parameters and the global FL model are transmitted over wireless links, wireless network performance will affect FL training performance. In particular, the number of resource blocks is limited; thus, the number of devices participating in FL is limited. Furthermore, edge nodes often have substantial constraints on their resources, such as memory, computation power, communication, and energy, severely limiting their capability to train large models locally. This paper proposes a two-hop communication protocol with a dynamic resource allocation strategy to investigate the possibility of bandwidth allocation from a limited network resource to the maximum number of clients participating in FL. In particular, we utilize an ordinary hierarchical FL with an adaptive grouping mechanism to select participating clients and elect a leader for each group based on its capability to upload the aggregated parameters to the central server. Our experimental results demonstrate that the proposed solution outperforms the baseline algorithm in terms of communication cost and model accuracy.https://ieeexplore.ieee.org/document/9924192/Federated learningdistributed machine learningmulti-hop wireless networkscommunication-efficiencybandwidth optimization |
| spellingShingle | Tu Viet Nguyen Nhan Duc Ho Hieu Thien Hoang Cuong Danh Do Kok-Seng Wong Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks IEEE Access Federated learning distributed machine learning multi-hop wireless networks communication-efficiency bandwidth optimization |
| title | Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks |
| title_full | Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks |
| title_fullStr | Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks |
| title_full_unstemmed | Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks |
| title_short | Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks |
| title_sort | toward efficient hierarchical federated learning design over multi hop wireless communications networks |
| topic | Federated learning distributed machine learning multi-hop wireless networks communication-efficiency bandwidth optimization |
| url | https://ieeexplore.ieee.org/document/9924192/ |
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