Thursday 23 January 2025
The quest for efficient communication in federated learning has led researchers to develop innovative solutions that can significantly reduce data transmission costs while maintaining model performance. A recent study has proposed an explanation-guided pruning strategy, which leverages layer-wise relevance propagation (LRP) to identify and eliminate less informative model parameters. This approach is particularly relevant for remote sensing image classification applications, where large-scale data processing and analysis require efficient communication.
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate on training a global model without sharing their local data. However, this process often involves transmitting large amounts of model updates between clients and the central server, leading to significant communication overhead. To address this issue, researchers have developed various strategies, including model compression, knowledge distillation, and pruning.
The proposed explanation-guided pruning strategy focuses on reducing communication overhead by identifying and removing less informative model parameters. LRP is used to attribute relevance scores to each component of the global model, allowing for the calculation of importance values. The least relevant components are then pruned from the model, resulting in a more efficient representation that requires fewer updates to be transmitted.
The effectiveness of this strategy was demonstrated through experiments on the BigEarthNet-S2 benchmark dataset, which consists of multi-label remote sensing images. Results showed that the proposed approach achieved higher mean average precision (mAP) scores compared to traditional federated learning training and random pruning strategies. Moreover, the computational cost of training the global model remained comparable to standard federated learning methods.
The significance of this study lies in its potential to improve the efficiency of remote sensing image classification applications, which often require processing large amounts of data across decentralized archives. By reducing communication overhead, researchers can accelerate the development and deployment of such systems, ultimately enabling more effective decision-making in fields such as environmental monitoring, disaster response, and urban planning.
The proposed strategy is also flexible and adaptable to various federated learning architectures and tasks, making it a valuable contribution to the field. As remote sensing applications continue to grow in complexity and scale, innovative solutions like this one will be crucial for ensuring efficient communication and data processing.
Cite this article: “Efficient Communication in Federated Learning for Remote Sensing Image Classification”, The Science Archive, 2025.
Federated Learning, Remote Sensing, Image Classification, Pruning, Model Compression, Knowledge Distillation, Layer-Wise Relevance Propagation, Communication Overhead, Efficient Communication, Multi-Label Images.
