Search Results for author: Zitao Li

Found 9 papers, 5 papers with code

Improving LoRA in Privacy-preserving Federated Learning

no code implementations18 Mar 2024 Youbang Sun, Zitao Li, Yaliang Li, Bolin Ding

Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency.

Computational Efficiency Federated Learning +1

A Bargaining-based Approach for Feature Trading in Vertical Federated Learning

no code implementations23 Feb 2024 Yue Cui, Liuyi Yao, Zitao Li, Yaliang Li, Bolin Ding, Xiaofang Zhou

We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives.

Vertical Federated Learning

FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

1 code implementation1 Sep 2023 Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou

When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.

Benchmarking Federated Learning +1

FS-Real: Towards Real-World Cross-Device Federated Learning

no code implementations23 Mar 2023 Daoyuan Chen, Dawei Gao, Yuexiang Xie, Xuchen Pan, Zitao Li, Yaliang Li, Bolin Ding, Jingren Zhou

Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry.

Federated Learning

Differentially Private Vertical Federated Clustering

2 code implementations2 Aug 2022 Zitao Li, Tianhao Wang, Ninghui Li

To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data.

Clustering Vertical Federated Learning

Estimating Numerical Distributions under Local Differential Privacy

2 code implementations2 Dec 2019 Zitao Li, Tianhao Wang, Milan Lopuhaä-Zwakenberg, Boris Skoric, Ninghui Li

When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator.

Locally Differentially Private Frequency Estimation with Consistency

1 code implementation20 May 2019 Tianhao Wang, Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Skoric, Ninghui Li

In this paper, we show that adding post-processing steps to FO protocols by exploiting the knowledge that all individual frequencies should be non-negative and they sum up to one can lead to significantly better accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values.

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability

no code implementations19 May 2018 Zitao Li, Jean Honorio

We introduce a new concept, data irrecoverability, and show that the well-studied concept of data privacy is sufficient but not necessary for data irrecoverability.

Cannot find the paper you are looking for? You can Submit a new open access paper.