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

arXiv:2610.00693 · cs.CV, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Aggregation — 38× in this paper · explained in 1 episode
• Adaptation — 23× in this paper · explained in 1 episode
• Alignment — 13× in this paper · explained in 1 episode
• Federated Learning — 12× in this paper · explained in 14 episodes
• Local Optimization — 5× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Non-IID Data — 4× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Computational overhead — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Alignment Scores — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Mechanism — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Heterogeneity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• EVALUATED COMPONENTS INCLUDE PRIVATE BATCH NORMALIZATION — 1× in this paper
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes

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