Poster: Hierarchical Clustered Federated Learning Framework for IoT in Remote Areas
Pages 863 - 864
Abstract
Unmanned Aerial Vehicles (UAVs) provide an effective solution for supporting Federated Learning (FL) in remote areas lacking infrastructure, but the non-IID nature of data affects FL efficiency. We propose a hierarchical clustering method with pipelined execution for UAV-assisted FL, clustering IoT devices based on computational capacity and data similarity to ensure balanced participation and faster convergence. Simulation results demonstrate improved learning efficiency and stability in non-IID environments, validating the method's effectiveness.
References
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Min Fu, Yuanming Shi, and Yong Zhou. 2024. Federated Learning via Unmanned Aerial Vehicle. IEEE Trans. Wirlel. Commun. 23, 4 (2024), 2884--2900.
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Gad Gad, Aya Farrag, Ahmed Aboulfotouh, Khaled Bedda, Zubair Md. Fadlullah, and Mostafa M. Fouda. 2024. Joint Self-Organizing Maps and Knowledge-Distillation-Based Communication-Efficient Federated Learning for Resource-Constrained UAV-IoT Systems. IEEE Internet Things J. 11, 9 (2024), 15504--15522.
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Yahao Ding, Zhaohui Yang, Quoc-Viet Pham, Ye Hu, Zhaoyang Zhang, and Mohammad Shikh-Bahaei. 2024. Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics. IEEE Internet Things J. 11, 5 (2024), 7447--7473.
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Cihat Keçeci, Mohammad Shaqfeh, Fawaz Al-Qahtani, Muhammad Ismail, and Erchin Serpedin. 2023. Clustered Scheduling and Communication Pipelining for Efficient Resource Management of Wireless Federated Learning. IEEE Internet Things J. 10, 15 (2023), 13303--13316.
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Su Wang, Roberto Morabito, Seyyedali Hosseinalipour, Mung Chiang, and Christopher G. Brinton. 2024. Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks. IEEE/ACM Trans. Netw. (2024), 1--17.
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Yupei Zhang, Shuangshuang Wei, Yifei Wang, Yunan Xu, Yuxin Li, and Xuequn Shang. 2022. A Personalized Federated Learning Framework Using Side Information for Heterogeneous Data Classification. IEEE BigData (2022), 3455--3461.
Index Terms
- Poster: Hierarchical Clustered Federated Learning Framework for IoT in Remote Areas
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Information
Published In
November 2024
950 pages
ISBN:9798400706974
DOI:10.1145/3666025
- Chair:
- Jie Liu,
- Co-chairs:
- Yuanchao Shu,
- Jiming Chen,
- Program Chair:
- Yuan He,
- Program Co-chair:
- Rui Tan
Copyright © 2024 Copyright held by the owner/author(s).
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Sponsors
- SIGARCH: ACM Special Interest Group on Computer Architecture
- SIGBED: ACM Special Interest Group on Embedded Systems
- SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
- SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
- SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 November 2024
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- Poster
Funding Sources
- National Research Foundation of Korea
Conference
SenSys '24
SenSys '24: 22nd ACM Conference on Embedded Networked Sensor Systems
November 4 - 7, 2024
Hangzhou, China
Acceptance Rates
Overall Acceptance Rate 174 of 867 submissions, 20%
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