2 citations · 8 across the 11 of their papers we have counts for
12 papers
Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States
Yifan Wang, Dejing Dou
Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persistent index-payload schism: dense v…
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
Ji Liu, Beichen Ma, Qiaolin Yu +7
Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices. The data distributed…
Efficient Federated Learning with Timely Update Dissemination
Juncheng Jia, Ji Liu, Chao Huo +4
Federated Learning (FL) has emerged as a compelling methodology for the management of distributed data, marked by significant advancements in recent years. In this paper, we propos…
AAPO: Enhancing the Reasoning Capabilities of LLMs with Advantage Margin
Jian Xiong, Jingbo Zhou, Jingyong Ye +2
Reinforcement learning (RL) has emerged as an effective approach for enhancing the reasoning capabilities of large language models (LLMs), especially in scenarios where supervised…
Trustworthy Federated Learning: Privacy, Security, and Beyond
Chunlu Chen, Ji Liu, Haowen Tan +5
While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative ap…
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models
Ji Liu, Jiaxiang Ren, Ruoming Jin +4
As a promising paradigm to collaboratively train models with decentralized data, Federated Learning (FL) can be exploited to fine-tune Large Language Models (LLMs). While LLMs corr…
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
Ji Liu, Juncheng Jia, Hong Zhang +5
Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this pape…
Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
Ji Liu, Chunlu Chen, Yu Li +5
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism…
Efficient Asynchronous Federated Learning with Sparsification and Quantization
Juncheng Jia, Ji Liu, Chendi Zhou +3
While data is distributed in multiple edge devices, Federated Learning (FL) is attracting more and more attention to collaboratively train a machine learning model without transfer…
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
Ji Liu, Tianshi Che, Yang Zhou +4
Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exc…
On Mask-based Image Set Desensitization with Recognition Support
Qilong Li, Ji Liu, Yifan Sun +2
In recent years, Deep Neural Networks (DNN) have emerged as a practical method for image recognition. The raw data, which contain sensitive information, are generally exploited wit…
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing
Dou Goodman, Xingjian Li, Ji Liu +2
Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversa…