Abstract
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 server. This makes FL a suitable learning paradigm for remote sensing (RS) image classification tasks, where data centralization may be restricted due to legal and privacy constraints. However, a key challenge in applying FL to RS tasks is the communication overhead caused by the frequent exchange of large model updates between clients and the central server. To address this issue, in this paper we propose a novel strategy (denoted as FedX) that uses explanation-guided pruning to reduce communication overhead by minimizing the size of the transmitted models without compromising performance. FedX leverages backpropagation-based explanation methods to estimate the task-specific importance of model components and prunes the least relevant ones at the central server. The resulting sparse global model is then sent to clients, substantially reducing communication overhead. We evaluate FedX on multi-label scene classification using the BigEarthNet-S2 dataset and single-label scene classification using the EuroSAT dataset. Experimental results show the success of FedX in significantly reducing the number of shared model parameters while enhancing the generalization capability of the global model, compared to both unpruned model and state-of-the-art pruning methods. The code of FedX will be available at this https URL.
Abstract (translated)
联邦学习（FL）使得分布式数据存储库（即客户端）之间可以协作训练深度神经网络，每个客户端本地存储数据，并仅与中央服务器共享模型更新。这使联邦学习成为遥感（RS）图像分类任务的合适学习范式，在这些任务中，由于法律和隐私限制的原因，可能无法集中化数据。然而，将联邦学习应用于遥感任务的关键挑战是频繁交换大型模型更新所导致的通信开销。为了解决这个问题，本文提出了一种新的策略（称为FedX），该策略利用解释引导剪枝来减少通信开销，在不影响性能的前提下最小化传输模型的大小。FedX采用基于反向传播的解释方法估计任务特定的模型组件的重要性，并在中央服务器上裁剪掉最不相关的组件。由此产生的稀疏全局模型随后被发送到客户端，显著减少了通信开销。 我们在多标签场景分类（使用BigEarthNet-S2数据集）和单标签场景分类（使用EuroSAT数据集）的任务中评估了FedX。实验结果表明，与未剪枝的模型及最先进的剪枝方法相比，FedX在大幅度减少共享模型参数数量的同时增强了全局模型的泛化能力。FedX的代码将在以下链接提供：[https URL]。
URL
https://arxiv.org/abs/2508.06256
