mlsanityData to 7 Oct 2026

User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks

arXiv 2309.09253 ↗First posted 17 Sep 2023cs.DC cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

The large population of wireless users is a key driver of data-crowdsourced Machine Learning (ML). However, data privacy remains a significant concern. Federated Learning (FL) encourages data sharing in ML without requiring data to leave users' devices but imposes heavy computation and communications overheads on mobile devices. Hierarchical FL (HFL) alleviates this problem by performing partial model aggregation at edge servers. HFL can effectively reduce energy consumption and latency through effective resource allocation and appropriate user assignment. Nevertheless, resource allocation in HFL involves optimizing multiple variables, and the objective function should consider both energy consumption and latency, making the development of resource allocation algorithms very complicated. Moreover, it is challenging to perform user assignment, which is a combinatorial optimization problem in a large search space. This article proposes a spectrum resource optimization algorithm (SROA) and a two-stage iterative algorithm (TSIA) for HFL. Given an arbitrary user assignment pattern, SROA optimizes CPU frequency, transmit power, and bandwidth to minimize system cost. TSIA aims to find a user assignment pattern that considerably reduces the total system cost. Experimental results demonstrate the superiority of the proposed HFL framework over existing studies in energy and latency reduction.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 43 references that are in the map. First posted on arXiv 17 Sep 2023.

Builds on

30 of 43 references.

References · 30

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  6. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  7. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  8. 2020fastai: A Layered API for Deep Learningcited by 183
  9. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  10. 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 129
  11. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  12. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
18 more
  1. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  2. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  3. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  4. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
  5. 2020Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applicationscited by 58
  6. 2020Wireless Federated Learning with Local Differential Privacycited by 50
  7. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  8. 2019A Crowdsourcing Framework for On-Device Federated Learningcited by 32
  9. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  10. 2022Privacy-Preserving Aggregation in Federated Learning: A Surveycited by 25
  11. 2020Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learningcited by 19
  12. 2022Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learningcited by 18
  13. 2020Hierarchical Federated Learning through LAN-WAN Orchestrationcited by 13
  14. 2020Delay Minimization for Federated Learning Over Wireless Communication Networkscited by 12
  15. 2020Enhancing Privacy via Hierarchical Federated Learningcited by 10
  16. 2021Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selectioncited by 7
  17. 2020Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecturecited by 2
  18. 2022Enhancing Federated Learning with spectrum allocation optimization and device selectioncited by 2

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6