面向神经网络架构自主演进的联邦协同遥感场景分类

黎健钊; 王善峰; 杨瑞; 唐泽栋; 周宇; 公茂果

doi:10.11834/jrs.20265223

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专辑

面向神经网络架构自主演进的联邦协同遥感场景分类
Federated neural architecture autonomous evolution for collaborative remote sensing scene classification
2026年30卷第5期页码：1498-1509
收稿：2025-06-23，

纸质出版：2026-05-07
DOI： 10.11834/jrs.20265223
稿件说明：

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黎健钊，王善峰，杨瑞，唐泽栋，周宇，公茂果.2026.面向神经网络架构自主演进的联邦协同遥感场景分类.遥感学报，30（5）： 1498-1509 DOI： 10.11834/jrs.20265223.

Li J Z，Wang S F，Yang R，Tang Z D，Zhou Y and Gong M G. 2026. Federated neural architecture autonomous evolution for collaborative remote sensing scene classification. National Remote Sensing Bulletin， 30（5）：1498-1509 DOI： 10.11834/jrs.20265223.

摘要

现有联邦遥感场景分类方法通常预设客户端的网络架构固定，忽略了卫星、无人机等设备在算力、存储与能源供应上的显著异构性，导致模型在资源受限终端难以适配部署，限制了联邦系统的实用效率。鉴于此，为探究支持神经网络架构在联邦框架下自主演化的协同方法，本文提出一种基于路径与数据协同采样的神经架构搜索方法。该方法采用“一次训练，多次使用”的神经网络架构搜索训练范式，通过路径与数据双维度协同采样机制实现客户端的个性化适配。在超网的协同训练阶段，各客户端通过梯度范数引导的路径采样动态筛选关键子网架构，基于梯度上界的数据采样聚焦高价值样本，大幅降低超网训练与通信成本；在子网多方部署阶段，通过进化算法基于本地数据搜索验证精度最优的个性化网络架构。此外，在4个基准异构遥感场景分类数据集上的实验验证结果表明，该方法在非独立同分布条件下显著优于固定架构与主流剪枝方法，能在保护数据隐私前提下为异构客户端协同演化出轻量高性能专属网络架构，有效提升了联邦遥感系统的部署可行性与整体性能。

Abstract

Existing federated remote sensing scene classification methods generally assume a fixed client network architecture

disregarding the significant heterogeneity in computing power

storage

and energy supply among devices such as satellites and drones. These limitations make deploying models on resource-constrained terminals difficult

restricting the practical efficiency of federated systems. Therefore

a novel federated remote sensing paradigm that is capable of dynamically adapting to the heterogeneity of client data and computational resources must be urgently developed. While safeguarding the data privacy of all parties

this paradigm should collaboratively explore optimal network architectures that are lighter

more computationally efficient

and tailored to the specific characteristics of each client’s local data. To address this bottleneck

this study proposes a federated collaborative framework with the autonomous evolution of neural network architectures. It proposes a neural architecture search mechanism based on path and data collaborative sampling to achieve lightweight

efficient

and personalized model construction that adapts to local data characteristics. During the collaborative training phase

each client dynamically screens key subnet architectures through gradient norm-guided path sampling and focuses on high-value samples through data sampling based on gradient upper bounds

significantly reducing supernetwork training and communication overhead. In the multiparty deployment phase

evolutionary algorithms are used to search for and validate the personalized subnet with the optimal verification accuracy on the basis of local data. The proposed framework’s foundational breakthrough lies in its integrated approach wherein gradient norm-guided path sampling dynamically identifies and prioritizes architecturally critical subnets during federated training. Simultaneously

gradient-capped data sampling concentrates computational resources on samples with significant effect on training outcomes. These mechanisms collectively form a synergistic strategy that substantially reduces gradient variance and training overhead across the hypernetwork while respecting client resource constraints. Following this collaborative hypernetwork development

each client autonomously executes evolutionary optimization

extracting customized subnetworks that are precisely tailored to local data distributions through adaptation-driven architecture exploration. By unifying adaptive sampling during training with evolutionary personalization while on deployment

the framework achieves unprecedented efficiency in generating lightweight but high-performance models. These models are optimized for diverse edge devices and their unique remote sensing environments while remaining within strict privacy-preserving federated parameters. Experiments on four benchmark heterogeneous remote sensing classification datasets

namely

AID

NWPU-RESISC45

PatternNet

and MEET

demonstrate that this method significantly outperforms fixed architectures and mainstream pruning methods under nonindependent and identically distributed conditions. It can evolve lightweight

high-performance

and dedicated network architectures for heterogeneous clients while protecting data privacy

effectively enhancing the deployment feasibility and overall performance of federated remote sensing systems. Ablation studies have confirmed that the integrated path-data sampling strategy is pivotal to these gains

reducing gradient variance and improving subnet consistency. This work resolves key bottlenecks in federated remote sensing systems by enabling resource-constrained clients to evolve specialized architectures autonomously. The integration of gradient-guided path sampling and data sampling optimizes training efficiency

while evolutionary optimization facilitate personalized subnet deployment. The proposed framework demonstrably enhances deployment feasibility and overall system performance without compromising data privacy

establishing an effective solution for heterogeneous federated learning in remote sensing scene classification.

关键词

Keywords

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