mlsanityData to 7 Oct 2026

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things

arXiv 2402.02506 ↗First posted 4 Feb 2024cs.DC cs.LGcited by 6

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Abstract (arXiv)

Federated Learning (FL) is a promising machine learning approach for Internet of Things (IoT), but it has to address network congestion problems when the population of IoT devices grows. Hierarchical FL (HFL) alleviates this issue by distributing model aggregation to multiple edge servers. Nevertheless, the challenge of communication overhead remains, especially in scenarios where all IoT devices simultaneously join the training process. For scalability, practical HFL schemes select a subset of IoT devices to participate in the training, hence the notion of device scheduling. In this setting, only selected IoT devices are scheduled to participate in the global training, with each of them being assigned to one edge server. Existing HFL assignment methods are primarily based on search mechanisms, which suffer from high latency in finding the optimal assignment. This paper proposes an improved K-Center algorithm for device scheduling and introduces a deep reinforcement learning-based approach for assigning IoT devices to edge servers. Experiments show that scheduling 50% of IoT devices is generally adequate for achieving convergence in HFL with much lower time delay and energy consumption. In cases where reduction in energy consumption (such as in Green AI) and reduction of messages (to avoid burst traffic) are key objectives, scheduling 30% IoT devices allows a substantial reduction in energy and messages with similar model accuracy.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 51 references that are in the map. First posted on arXiv 4 Feb 2024.

Builds on

24 of 51 references.

References · 24

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  7. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  8. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  9. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  10. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  11. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  12. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
12 more
  1. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 74
  2. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  3. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  4. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  5. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  6. 2021AI-Native Network Slicing for 6G Networkscited by 31
  7. 2022Privacy-Preserving Aggregation in Federated Learning: A Surveycited by 25
  8. 2020Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Servicescited by 18
  9. 2022Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learningcited by 18
  10. 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
  11. 2020Delay Minimization for Federated Learning Over Wireless Communication Networkscited by 12
  12. 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  2. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  3. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  4. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
  5. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0
  6. 2026Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environmentscited by 0