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FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine
  Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation
  Models with Mobile Edge Computing

FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing

26 October 2023
Terence Jie Chua
Wen-li Yu
Junfeng Zhao
Kwok-Yan Lam
    FedML
ArXivPDFHTML

Papers citing "FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing"

4 / 4 papers shown
Title
PrivTuner with Homomorphic Encryption and LoRA: A P3EFT Scheme for
  Privacy-Preserving Parameter-Efficient Fine-Tuning of AI Foundation Models
PrivTuner with Homomorphic Encryption and LoRA: A P3EFT Scheme for Privacy-Preserving Parameter-Efficient Fine-Tuning of AI Foundation Models
Yang Li
Wenhan Yu
Jun Zhao
27
1
0
01 Oct 2024
Federated Large Language Models: Current Progress and Future Directions
Federated Large Language Models: Current Progress and Future Directions
Yuhang Yao
Jianyi Zhang
Junda Wu
Chengkai Huang
Yu Xia
...
Ang Li
Lina Yao
Julian McAuley
Yiran Chen
Carlee Joe-Wong
FedML
AIFin
63
8
0
24 Sep 2024
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally
  Across Scales and Tasks
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
Xiao Liu
Kaixuan Ji
Yicheng Fu
Weng Lam Tam
Zhengxiao Du
Zhilin Yang
Jie Tang
VLM
238
806
0
14 Oct 2021
The Power of Scale for Parameter-Efficient Prompt Tuning
The Power of Scale for Parameter-Efficient Prompt Tuning
Brian Lester
Rami Al-Rfou
Noah Constant
VPVLM
280
3,848
0
18 Apr 2021
1