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Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning

arXiv 2311.02130 ↗First posted 3 Nov 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client orchestration policy considering client heterogenerity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 39 references that are in the map. First posted on arXiv 3 Nov 2023.

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17 of 39 references.

References · 17

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2018Federated Learning with Non-IID Datacited by 1,099
  3. 2019Towards Federated Learning at Scale: System Designcited by 826
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  5. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  6. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  7. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  8. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  9. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  10. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  11. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  12. 2018Toward Understanding the Impact of Staleness in Distributed Machine Learningcited by 28
5 more
  1. 2015Distributed Optimization with Arbitrary Local Solverscited by 23
  2. 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
  3. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  4. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  5. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8

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