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

TL;DR

This paper introduces a novel learner referral and resource scheduling framework for federated learning in hierarchical IoT networks, optimizing cost, fairness, and accuracy through joint and distributed methods.

Contribution

It proposes a new joint learner referral and resource scheduling framework with both centralized and distributed algorithms for federated learning in hierarchical IoT networks.

Findings

Achieves a balance between high global accuracy and reduced cost.

Demonstrates effectiveness on MNIST and CIFAR-10 datasets.

Provides scalable distributed learner referral approach.

Abstract

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.