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Found 4 papers, 4 papers with code
Resource-Efficient Transformer Pruning for Finetuning of Large Models
With the recent advances in vision transformers and large language models (LLMs) finetuning costly large models on downstream learning tasks poses significant challenges under limited computational resources.
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
In most FL approaches, all edge clients are assumed to have sufficient computation capabilities to participate in the learning of a deep neural network (DNN) model.
Gradient-Leakage Resilient Federated Learning
This paper presents a gradient leakage resilient approach to privacy-preserving federated learning with per training example-based client differential privacy, coined as Fed-CDP.
Compiling ONNX Neural Network Models Using MLIR
Deep neural network models are becoming increasingly popular and have been used in various tasks such as computer vision, speech recognition, and natural language processing.
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