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A Hierarchical Federated Learning Approach for the Internet of Things

arXiv 2403.01540 ↗First posted 3 Mar 2024cs.LG cs.IT math.ITcited by 0

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Abstract (arXiv)

This paper presents a novel federated learning solution, QHetFed, suitable for large-scale Internet of Things deployments, addressing the challenges of large geographic span, communication resource limitation, and data heterogeneity. QHetFed is based on hierarchical federated learning over multiple device sets, where the learning process and learning parameters take the necessary data quantization and the data heterogeneity into consideration to achieve high accuracy and fast convergence. Unlike conventional hierarchical federated learning algorithms, the proposed approach combines gradient aggregation in intra-set iterations with model aggregation in inter-set iterations. We offer a comprehensive analytical framework to evaluate its optimality gap and convergence rate, and give a closed form expression for the optimal learning parameters under a deadline, that accounts for communication and computation times. Our findings reveal that QHetFed consistently achieves high learning accuracy and significantly outperforms other hierarchical algorithms, particularly in scenarios with heterogeneous data distributions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 3 Mar 2024.

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26 of 44 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 539
  4. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  5. 2019Federated Learning over Wireless Fading Channelscited by 183
  6. 2019Peer-to-peer Federated Learning on Graphscited by 85
  7. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  8. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  9. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  10. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  11. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  12. 2015Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Studycited by 19
14 more
  1. 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
  2. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  3. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  4. 2021FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learningcited by 11
  5. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  6. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
  7. 2023Adaptive Federated Pruning in Hierarchical Wireless Networkscited by 10
  8. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  9. 2023Delay-Aware Hierarchical Federated Learningcited by 9
  10. 2024Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneitycited by 6
  11. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
  12. 2023Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfercited by 4
  13. 2023Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noisecited by 4
  14. 2016A Hierarchical Distributed Processing Framework for Big Image Datacited by 1

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  7. 2023Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation
  8. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design