Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification
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.
Code (1)
Tasks
Federated Learningimage-classificationImage ClassificationRemote Sensing Image ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing
Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients), where each client stores data locally and only shares model updates with a central se…
Scene ClassificationImage ClassificationFederated LearningFedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
Person re-identification (re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) provides a privacy-preserving paradigm for collaborative model training without centralized da…
Person Re-IdentificationFederated LearningCA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction
Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence. We present Curvature-Aware Heterogeneous Federated Pruning (CA-HFP), a…
Federated LearningSAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning
Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they ofte…
Federated LearningPersonalized Federated LearningOptimizing Federated Learning using Remote Embeddings for Graph Neural Networks
Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machin…
Federated Learning