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The paradigm of federated learning (FL) to address data privacy concerns bylocally training parameters on resource-constrained clients in a distributedmanner has garnered significant attention. Nonetheless, FL is not applicablewhen not all clients within the coverage of the FL server are registered withthe FL network. To bridge this gap, this paper proposes joint learner referralaided federated client selection (LRef-FedCS), along with communications andcomputing resource scheduling, and local model accuracy optimization (LMAO)methods. These methods are designed to minimize the cost incurred by theworst-case participant and ensure the long-term fairness of FL in hierarchicalInternet of Things (HieIoT) networks. Utilizing the Lyapunov optimizationtechnique, we reformulate the original problem into a stepwise jointoptimization problem (JOP). Subsequently, to tackle the mixed-integernon-convex JOP, we separatively and iteratively address LRef-FedCS and LMAOthrough the centralized method and self-adaptive global best harmony search(SGHS) algorithm, respectively. To enhance scalability, we further propose adistributed LRef-FedCS approach based on a matching game to replace thecentralized method described above. Numerical simulations and experimentalresults on the MNIST/CIFAR-10 datasets demonstrate that our proposed LRef-FedCSapproach could achieve a good balance between pursuing high global accuracy andreducing cost.

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