5 papers
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…
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…
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…