H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications
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| Publicat a: | arXiv.org (Dec 9, 2024), p. n/a |
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| Autor principal: | |
| Altres autors: | , , |
| Publicat: |
Cornell University Library, arXiv.org
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3142727152 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3142727152 | ||
| 045 | 0 | |b d20241209 | |
| 100 | 1 | |a Gao, Jiechao | |
| 245 | 1 | |a H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 9, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a The proliferation of Internet of Things (IoT) has increased interest in federated learning (FL) for privacy-preserving distributed data utilization. However, traditional two-tier FL architectures inadequately adapt to multi-tier IoT environments. While Hierarchical Federated Learning (HFL) improves practicality in multi-tier IoT environments by multi-layer aggregation, it still faces challenges in communication efficiency and accuracy due to high data transfer volumes, data heterogeneity, and imbalanced device distribution, struggling to meet the low-latency and high-accuracy model training requirements of practical IoT scenarios. To overcome these limitations, we propose H-FedSN, an innovative approach for practical IoT environments. H-FedSN introduces a binary mask mechanism with shared and personalized layers to reduce communication overhead by creating a sparse network while keeping original weights frozen. To address data heterogeneity and imbalanced device distribution, we integrate personalized layers for local data adaptation and apply Bayesian aggregation with cumulative Beta distribution updates at edge and cloud levels, effectively balancing contributions from diverse client groups. Evaluations on three real-world IoT datasets and MNIST under non-IID settings demonstrate that H-FedSN significantly reduces communication costs by 58 to 238 times compared to HierFAVG while achieving high accuracy, making it highly effective for practical IoT applications in hierarchical federated learning scenarios. | |
| 653 | |a Data transfer (computers) | ||
| 653 | |a Accuracy | ||
| 653 | |a Internet of Things | ||
| 653 | |a Multilayers | ||
| 653 | |a Federated learning | ||
| 653 | |a Communication | ||
| 653 | |a Probability distribution functions | ||
| 653 | |a Customization | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Network latency | ||
| 700 | 1 | |a Li, Yuangang | |
| 700 | 1 | |a Zhao, Yue | |
| 700 | 1 | |a Campbell, Brad | |
| 773 | 0 | |t arXiv.org |g (Dec 9, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3142727152/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.06210 |