Time Minimization in Hierarchical Federated Learning

TL;DR

This paper proposes an optimization framework for hierarchical federated learning that minimizes communication and computation delays, leading to faster model convergence and reduced latency through joint parameter tuning and UE-to-edge association strategies.

Contribution

It introduces a novel joint optimization approach for delay minimization in hierarchical federated learning, including algorithms for iteration count tuning and UE-to-edge association.

Findings

Faster global model convergence with optimal iteration counts.

Reduced system latency using the proposed UE-to-edge association.

Effective delay minimization demonstrated through simulations.

Abstract

Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts. To solve the problem, an iterative algorithm is proposed. After that, a time-minimized UE-to-edge association algorithm is presented where the maximum latency of the system is reduced. Simulation results show that the global model converges faster under optimal edge server and local iteration counts. The hierarchical federated learning latency is minimized with the proposed UE-to-edge association strategy.