FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRI
eess.IV, cs.CV
Submitted: 2026-07-20
Updated: 2026-07-20
Terminology
Sources
- An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging
- Mitigating Data Heterogeneity in Federated Learning with Data Augmentation
- Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
- Single Shot Structured Pruning Before Training
- The LLM Surgeon
- Iterative Activation-based Structured Pruning
- An Entropy-based Pruning Method for CNN Compression
- Pruning Convolutional Neural Networks for Resource Efficient Inference
- Filter Pruning based on Information Capacity and Independence
- The Federated Tumor Segmentation (FeTS) Challenge
- OpenFL: An open-source framework for Federated Learning
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