arXiv：cs.LG（机器学习，全量分类）· Bar{\i}\c{s} B\"uy\"ukta\c{s}, Beg\"um Demir·· 13 小时前AI 评分25
FedMAD：面向遥感图像分类的调制感知定向聚合联邦学习框架
FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification
AI 导读
研究者提出个性化联邦学习框架 FedMAD，用于遥感图像分类，通过将全局共享表示参数与客户端特定适配参数分离，并在骨干网络中引入轻量调制模块和本地批归一化层来应对客户端数据分布异构问题。
正文
Abstract:Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentralized RS image archives without requiring direct access to local data. However, FL performance significantly degrades when the data distributions between clients are heterogeneous, which often occurs due to geographical differences, seasonal changes, and varying image acquisition and atmospheric conditions. To address this challenge, in this letter, we propose a novel personalized FL framework (denoted as FedMAD) for RS image classification problems. The proposed framework separates globally shared representation parameters from client-specific adaptation parameters to preserve client-specific features while maintaining globally transferable representations. This is achieved by integrating lightweight modulation modules and local batch normalization layers into the backbone network. Although globally shared parameters are collaboratively optimized between clients, client-specific parameters remain local to preserve domain-specific feature characteristics. In addition, FedMAD introduces a modulation-aware directional aggregation strategy that dynamically adjusts the importance of aggregation for each client according to the alignment of local modulation updates. This allows the global optimization process to suppress conflicting client updates originating from heterogeneous data distributions while enhancing the contribution of clients with consistent adaptation behaviors. The experimental results obtained on the BigEarthNet-S2 and EuroSAT datasets demonstrate the effectiveness of FedMAD compared to state-of-the-art FL algorithms under heterogeneous RS data distributions. The code of the proposed framework will be publicly available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00693 [cs.CV] |
| (or arXiv:2610.00693v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00693 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Baris Buyuktas [view email]
[v1]
Wed, 30 Sep 2026 20:36:37 UTC (604 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org