Sixing Yu | DeepAI

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∙ 05/19/2023

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Foundation Models (FMs), such as BERT, GPT, ViT, and CLIP, have demonstr...

0 Sixing Yu, et al. ∙

∙ 12/13/2022

Towards Seamless Management of AI Models in High-Performance Computing

With the increasing prevalence of artificial intelligence (AI) in divers...

0 Sixing Yu, et al. ∙

∙ 11/09/2022

Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search

Federated Learning (FL) is extensively used to train AI/ML models in dis...

0 Sixing Yu, et al. ∙

∙ 08/16/2022

Resource-aware Federated Learning using Knowledge Extraction and Multi-model Fusion

With increasing concern about user data privacy, federated learning (FL)...

0 Sixing Yu, et al. ∙

∙ 11/29/2021

SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning

Efficient federated learning is one of the key challenges for training a...

5 Sixing Yu, et al. ∙

∙ 06/13/2021

Adaptive Dynamic Pruning for Non-IID Federated Learning

Federated Learning (FL) has emerged as a new paradigm of training machin...

17 Sixing Yu, et al. ∙

∙ 02/05/2021

GNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning

Model compression is an essential technique for deploying deep neural ne...

0 Sixing Yu, et al. ∙

∙ 11/25/2020

Auto Graph Encoder-Decoder for Model Compression and Network Acceleration

Model compression aims to deploy deep neural networks (DNN) to mobile de...

0 Sixing Yu, et al. ∙

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