In federated remote sensing, LoRA tuning of a frozen CLIP model achieves the best accuracy-to-communication trade-off, while full fine-tuning causes severe catastrophic forgetting of pretrained knowledge.
FedKL: Tackling data heterogeneity in federated reinforcement learning by penalizing KL divergence,
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On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing
In federated remote sensing, LoRA tuning of a frozen CLIP model achieves the best accuracy-to-communication trade-off, while full fine-tuning causes severe catastrophic forgetting of pretrained knowledge.