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

arXiv: https://arxiv.org/abs/2307.09977 · HTML: https://mlsanity.com/paper/2307.09977/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [Central Server Free Federated Learning over Single-sided Trust Social Networks](https://mlsanity.com/paper/1910.04956/) (2019)
- [Distributed Optimization with Arbitrary Local Solvers](https://mlsanity.com/paper/1512.04039/) (2015)
- [Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis](https://mlsanity.com/paper/2209.12274/) (2022)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [Toward Intelligent Vehicular Networks: A Machine Learning Framework](https://mlsanity.com/paper/1804.00338/) (2018)
- [Reflection Resource Management for Intelligent Reflecting Surface Aided Wireless Networks](https://mlsanity.com/paper/2002.00331/) (2020)

## Built on by (most cited first, 1 of 1)

- [PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs](https://mlsanity.com/paper/2607.12111/) (2026)

## 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.