A Hierarchical Federated Learning Approach for the Internet of Things

TL;DR

This paper introduces QHetFed, a hierarchical federated learning method tailored for large-scale IoT systems, effectively handling data heterogeneity and communication constraints to improve accuracy and convergence.

Contribution

The paper proposes a novel hierarchical federated learning algorithm, QHetFed, that integrates gradient and model aggregation, with an analytical framework for optimal parameter tuning.

Findings

QHetFed achieves high accuracy in heterogeneous data scenarios.

It outperforms existing hierarchical algorithms in convergence speed.

The approach effectively balances communication and computation times.

Abstract

This paper presents a novel federated learning solution, QHetFed, suitable for large-scale Internet of Things deployments, addressing the challenges of large geographic span, communication resource limitation, and data heterogeneity. QHetFed is based on hierarchical federated learning over multiple device sets, where the learning process and learning parameters take the necessary data quantization and the data heterogeneity into consideration to achieve high accuracy and fast convergence. Unlike conventional hierarchical federated learning algorithms, the proposed approach combines gradient aggregation in intra-set iterations with model aggregation in inter-set iterations. We offer a comprehensive analytical framework to evaluate its optimality gap and convergence rate, and give a closed form expression for the optimal learning parameters under a deadline, that accounts for communication and computation times. Our findings reveal that QHetFed consistently achieves high learning accuracy and significantly outperforms other hierarchical algorithms, particularly in scenarios with heterogeneous data distributions.