mlsanityData to 7 Oct 2026

Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

arXiv 2406.14910 ↗First posted 21 Jun 2024cs.LG cs.DC math.OCcited by 6

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Abstract (arXiv)

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This paper presents a new two-phase deep deterministic policy gradient (DDPG) framework, referred to as ``TP-DDPG'', to balance online the learning delay and model accuracy of an FL process in an energy harvesting-powered HFL system. The key idea is that we divide optimization decisions into two groups, and employ DDPG to learn one group in the first phase, while interpreting the other group as part of the environment to provide rewards for training the DDPG in the second phase. Specifically, the DDPG learns the selection of participating clients, and their CPU configurations and the transmission powers. A new straggler-aware client association and bandwidth allocation (SCABA) algorithm efficiently optimizes the other decisions and evaluates the reward for the DDPG. Experiments demonstrate that with substantially reduced number of learnable parameters, the TP-DDPG can quickly converge to effective polices that can shorten the training time of HFL by 39.4% compared to its benchmarks, when the required test accuracy of HFL is 0.9.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 58 references that are in the map. First posted on arXiv 21 Jun 2024.

Builds on

22 of 58 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  4. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  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. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  8. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  9. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  10. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  11. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  12. 2022Machine Learning Model Sizes and the Parameter Gapcited by 39
10 more
  1. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  2. 2020Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applicationscited by 21
  3. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  4. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  5. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  6. 2021Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learningcited by 10
  7. 2021Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Datacited by 10
  8. 2023Time-sensitive Learning for Heterogeneous Federated Edge Intelligencecited by 8
  9. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
  10. 2022Meta-material Sensor Based Internet of Things: Design, Optimization, and Implementationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
  2. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  3. 2026Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregationcited by 1
  4. 2025Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodologycited by 0
  5. 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learningcited by 0
  6. 2026Geometric Fairness-Aware Routing for Federated Edge Networkscited by 0