20 citations · 71 across the 14 of their papers we have counts for
14 papers
KIMAs: A Configurable Knowledge Integrated Multi-Agent System
Zitao Li, Fei Wei, Yuexiang Xie +6
Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects…
Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning
Pengfei He, Zitao Li, Yue Xing +3
Zero-shot reasoning methods with Large Language Models (LLMs) offer significant advantages including great generalization to novel tasks and reduced dependency on human-crafted exa…
VertiMRF: Differentially Private Vertical Federated Data Synthesis
Fangyuan Zhao, Zitao Li, Xuebin Ren +3
Data synthesis is a promising solution to share data for various downstream analytic tasks without exposing raw data. However, without a theoretical privacy guarantee, a synthetic…
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
Feijie Wu, Zitao Li, Yaliang Li +2
Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, many domain-specific data are privately d…
Improving LoRA in Privacy-preserving Federated Learning
Youbang Sun, Zitao Li, Yaliang Li +1
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 compu…
On the Robustness of LDP Protocols for Numerical Attributes under Data Poisoning Attacks
Xiaoguang Li, Zitao Li, Ninghui Li +1
Recent studies reveal that local differential privacy (LDP) protocols are vulnerable to data poisoning attacks where an attacker can manipulate the final estimate on the server by…
AgentScope: A Flexible yet Robust Multi-Agent Platform
Dawei Gao, Zitao Li, Xuchen Pan +16
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooper…
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
Weirui Kuang, Bingchen Qian, Zitao Li +7
LLMs have demonstrated great capabilities in various NLP tasks. Different entities can further improve the performance of those LLMs on their specific downstream tasks by fine-tuni…
FS-Real: Towards Real-World Cross-Device Federated Learning
Daoyuan Chen, Dawei Gao, Yuexiang Xie +5
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 bot…
Differentially Private Vertical Federated Clustering
Zitao Li, Tianhao Wang, Ninghui Li
In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a mode…
Estimating Numerical Distributions under Local Differential Privacy
Zitao Li, Tianhao Wang, Milan Lopuhaä-Zwakenberg +2
When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before se…
Improving Frequency Estimation under Local Differential Privacy
Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Škorić +1
Local Differential Privacy protocols are stochastic protocols used in data aggregation when individual users do not trust the data aggregator with their private data. In such proto…
Locally Differentially Private Frequency Estimation with Consistency
Tianhao Wang, Milan Lopuhaä-Zwakenberg, Zitao Li +2
Local Differential Privacy (LDP) protects user privacy from the data collector. LDP protocols have been increasingly deployed in the industry. A basic building block is frequency o…
Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability
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. We show that th…