FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification
A novel personalized FL framework (denoted as FedMAD) for RS image classification problems that 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.