FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification

2026-09-30

AI Summary

This study addresses the performance degradation of federated learning in remote sensing image classification caused by data heterogeneity. To this end, we propose a personalized federated learning framework that decouples globally shared and client-specific parameters, thereby preserving domain characteristics while maintaining generalizable representations. Furthermore, a modulation-aware directional aggregation strategy is introduced to dynamically adjust weights, suppressing conflicting updates and reinforcing consistent contributions. The proposed method achieves efficient personalized modeling by integrating lightweight modulation modules with local batch normalization. Extensive experiments on the BigEarthNet-S2 and EuroSAT datasets demonstrate that our approach significantly outperforms existing algorithms, effectively overcoming non-independent and identically distributed (non-IID) challenges in federated remote sensing scenarios.

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 https://git.tu-berlin.de/rsim/fedmad.

Problem

• Federated Learning
• Remote Sensing Image Classification
• Data Heterogeneity
• Personalized Federated Learning

Innovation

• Federated Learning
• Personalized Framework
• Modulation-Aware Directional Aggregation
• Remote Sensing Image Classification
• Parameter Decoupling