mlsanityData to 7 Oct 2026

Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks

arXiv 2307.09977 ↗First posted 19 Jul 2023cs.LG cs.NIcited by 1

Not on the map. Why?

Abstract (arXiv)

The paradigm of federated learning (FL) to address data privacy concerns by locally training parameters on resource-constrained clients in a distributed manner has garnered significant attention. Nonetheless, FL is not applicable when not all clients within the coverage of the FL server are registered with the FL network. To bridge this gap, this paper proposes joint learner referral aided federated client selection (LRef-FedCS), along with communications and computing resource scheduling, and local model accuracy optimization (LMAO) methods. These methods are designed to minimize the cost incurred by the worst-case participant and ensure the long-term fairness of FL in hierarchical Internet of Things (HieIoT) networks. Utilizing the Lyapunov optimization technique, we reformulate the original problem into a stepwise joint optimization problem (JOP). Subsequently, to tackle the mixed-integer non-convex JOP, we separatively and iteratively address LRef-FedCS and LMAO through the centralized method and self-adaptive global best harmony search (SGHS) algorithm, respectively. To enhance scalability, we further propose a distributed LRef-FedCS approach based on a matching game to replace the centralized method described above. Numerical simulations and experimental results on the MNIST/CIFAR-10 datasets demonstrate that our proposed LRef-FedCS approach could achieve a good balance between pursuing high global accuracy and reducing cost.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 53 references that are in the map. First posted on arXiv 19 Jul 2023.

Builds on

16 of 53 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  4. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  5. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  6. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  7. 2019Reliable Federated Learning for Mobile Networkscited by 92
  8. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  9. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 61
  10. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  11. 2019Central Server Free Federated Learning over Single-sided Trust Social Networkscited by 33
  12. 2015Distributed Optimization with Arbitrary Local Solverscited by 23
4 more
  1. 2022Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysiscited by 23
  2. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  3. 2018Toward Intelligent Vehicular Networks: A Machine Learning Frameworkcited by 11
  4. 2020Reflection Resource Management for Intelligent Reflecting Surface Aided Wireless Networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMscited by 0