71 citations · 84 across the 9 of their papers we have counts for
9 papers
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…
DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task
Honglin Xiong, Sheng Wang, Yitao Zhu +5
The recent progress of large language models (LLMs), including ChatGPT and GPT-4, in comprehending and responding to human instructions has been remarkable. Nevertheless, these mod…
Towards Efficient Communications in Federated Learning: A Contemporary Survey
Zihao Zhao, Yuzhu Mao, Yang Liu +4
In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privac…
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…
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
Yuzhu Mao, Zihao Zhao, Meilin Yang +5
Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency…
Communication Efficient Federated Learning with Adaptive Quantization
Yuzhu Mao, Zihao Zhao, Guangfeng Yan +4
Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy preserving measures and great potentials in some distributed but privacy-sensitive ap…