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

Excerpt

arXiv:2610.00693v1 Announce Type: cross 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 c