71 citations · 84 across the 9 of their papers we have counts for
5 papers · 1 filter
Enhancing Parameter Efficiency and Generalization in Large-Scale Models: A Regularized and Masked Low-Rank Adaptation Approach
Yuzhu Mao, Siqi Ping, Zihao Zhao +2
Large pre-trained models, such as large language models (LLMs), present significant resource challenges for fine-tuning due to their extensive parameter sizes, especially for appli…
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt
Zihao Zhao, Zhenpeng Shi, Yang Liu +1
Federated Learning (FL) is often impeded by communication overhead issues. Prompt tuning, as a potential solution, has been introduced to only adjust a few trainable parameters rat…
Federated PAC-Bayesian Learning on Non-IID data
Zihao Zhao, Yang Liu, Wenbo Ding +1
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while…
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
Zihao Zhao, Yuzhu Mao, Zhenpeng Shi +4
The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typica…
Deep Leakage from Model in Federated Learning
Zihao Zhao, Mengen Luo, Wenbo Ding
Distributed machine learning has been widely used in recent years to tackle the large and complex dataset problem. Therewith, the security of distributed learning has also drawn in…