发表机构
Technische Universität Berlin; BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林工业大学; 柏林学习与数据基础研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对遥感图像分类中联邦学习因数据异构导致性能下降的问题，提出FedMAD框架，通过分离全局与客户端参数并采用调制感知方向聚合策略，在BigEarthNet-S2和EuroSAT数据集上验证了其优于现有方法。
AI 中文摘要
联邦学习（FL）近年来在遥感（RS）领域引起了越来越多的关注，因为它能够在无需直接访问本地数据的情况下，跨分散的遥感图像档案库实现协作模型训练。然而，当客户端之间的数据分布异构时，联邦学习的性能会显著下降，这种情况通常由于地理差异、季节变化以及图像采集和大气条件的不同而发生。为了解决这一挑战，在本信中，我们提出了一种新颖的个性化联邦学习框架（记为FedMAD），用于遥感图像分类问题。该框架将全局共享的表示参数与客户端特定的适应参数分离，以在保持全局可迁移表示的同时保留客户端特定的特征。这是通过将轻量级调制模块和局部批归一化层集成到骨干网络中实现的。尽管全局共享参数在客户端之间协作优化，但客户端特定参数保持本地化以保留领域特定的特征特性。此外，FedMAD引入了一种调制感知的方向聚合策略，该策略根据局部调制更新的对齐情况动态调整每个客户端的聚合重要性。这使得全局优化过程能够抑制由异构数据分布引起的冲突客户端更新，同时增强具有一致适应行为的客户端的贡献。在BigEarthNet-S2和EuroSAT数据集上获得的实验结果表明，在异构遥感数据分布下，与最先进的联邦学习算法相比，FedMAD的有效性。所提出框架的代码将在该https URL上公开提供。
英文摘要
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 https://git.tu-berlin.de/rsim/fedmad.
