FedShield-LLM integrates pruning and FHE on LoRA parameters to support secure, scalable federated fine-tuning of LLMs such as Llama-2.
Federated optimization in heterogeneous networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Balanced synthetic image augmentation via GANs and diffusion models raises average AUC from 0.9206 to 0.9362 for FedAvg and 0.9429 to 0.9574 for FedProx in federated breast ultrasound classification.
citing papers explorer
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FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
FedShield-LLM integrates pruning and FHE on LoRA parameters to support secure, scalable federated fine-tuning of LLMs such as Llama-2.
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Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Balanced synthetic image augmentation via GANs and diffusion models raises average AUC from 0.9206 to 0.9362 for FedAvg and 0.9429 to 0.9574 for FedProx in federated breast ultrasound classification.