Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification

TL;DR

This paper proposes an explanation-guided pruning method for federated learning in remote sensing image classification, significantly reducing communication costs while maintaining or improving model performance.

Contribution

It introduces a layer-wise relevance propagation-based pruning strategy to select the most informative model parameters for communication in federated learning.

Findings

Reduces volume of model updates transmitted in federated learning.

Improves global model generalization on remote sensing data.

Effectively identifies and removes non-informative model parameters.

Abstract

Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a global model by exchanging only model updates with the central server without sharing the local data of the clients. Due to the large volume of model updates required to be transmitted between clients and the central server, most FL systems are associated with high transfer costs (i.e., communication overhead). This issue is more critical for operational applications in remote sensing (RS), especially when large-scale RS data is processed and analyzed through FL systems with restricted communication bandwidth. To address this issue, we introduce an explanation-guided pruning strategy for communication-efficient FL in the context of RS image classification. Our pruning strategy is defined based on the layer-wise relevance propagation (LRP) driven explanations to: 1) efficiently and effectively identify the most relevant and informative model parameters (to be exchanged between clients and the central server); and 2) eliminate the non-informative ones to minimize the volume of model updates. The experimental results on the BigEarthNet-S2 dataset demonstrate that our strategy effectively reduces the number of shared model updates, while increasing the generalization ability of the global model. The code of this work is publicly available at https://git.tu-berlin.de/rsim/FL-LRP.