1 citations · 1 across the 10 of their papers we have counts for
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Exploring Selective Layer Fine-Tuning in Federated Learning
Yuchang Sun, Yuexiang Xie, Bolin Ding +2
Federated learning (FL) has emerged as a promising paradigm for fine-tuning foundation models using distributed data in a privacy-preserving manner. Under limited computational res…
Very Large-Scale Multi-Agent Simulation in AgentScope
Xuchen Pan, Dawei Gao, Yuexiang Xie +6
Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations. However, there remain several challenges w…
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…
Understanding Byzantine Robustness in Federated Learning with A Black-box Server
Fangyuan Zhao, Yuexiang Xie, Xuebin Ren +3
Federated learning (FL) becomes vulnerable to Byzantine attacks where some of participators tend to damage the utility or discourage the convergence of the learned model via sendin…
The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective
Zhen Qin, Daoyuan Chen, Wenhao Zhang +5
The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a br…
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…