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Found 9 papers, 2 papers with code
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning
This is evident across tasks in both natural language processing and computer vision domains.
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amounts of data for pre-training.
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
RaFL allocates resource-aware models to edge devices using Neural Architecture Search~(NAS) and allows heterogeneous model architecture deployment by knowledge extraction and fusion.
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion
This method allows efficient multi-model knowledge fusion and the deployment of resource-aware models while preserving model heterogeneity.
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Federated learning~(FL) facilitates the training and deploying AI models on edge devices.
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient
Our approach reduces up to 50\% FLOPs inference of DNNs on edge devices while maintaining the model's quality.
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
Model compression is an essential technique for deploying deep neural networks (DNNs) on power and memory-constrained resources.
Auto Graph Encoder-Decoder for Neural Network Pruning
We compared our method with rule-based DNN embedding model compression methods to show the effectiveness of our method.
