Yin Xu

MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing

Chuang Tang, Chenhao Lin, Yin Xu, Hao Wang et autres

Parsing chemical reaction diagrams from scientific literature is challenging due to heterogeneous layouts, intertwined visual elements, and difficulty in integrating visual recognition with chemically consistent reasoning. Existing Vision Language Models advance multimodal understanding but remain unreliable on complex reaction diagrams, where spatial …

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Multimodal deep learning framework for Alzheimer’s stage classification using GAN-augmented MRI and clinical data

Talha Ahmad, Ghulam Hussain Noori, Abdul Wahab, Yin Xu

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Federated Learning Framework with Personalized Model Compression and Privacy Protection

Mingjun Xiao, Yin Xu, Jie Wu

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Optimizing Data-Driven Federated Learning in UAV Networks

Datian Li, Mingjun Xiao, Yin Xu, Jie Ca Wu

Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned …

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Q2oE: Balancing QoE Fairness and Preference for Video Streaming from Cooperative Edge Servers

Xinwei Huang, Yin Xu, Jinbo Cai, He Sun et autres

Quality of Experience (QoE) is a pivotal factor that defines the overall quality within the realm of video streaming services. To improve users' QoE indicators, existing methods have proposed many QoE models and Adaptive Bitrate (ABR) algorithms. However, these advanced methods do …

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Enhancing Decentralized Federated Learning With Model Pruning and Adaptive Communication

Yin Xu, Mingjun Xiao, Jie Wu, Guoju Gao et autres

Federated learning (FL) is a distributed learning paradigm that enables large-scale IoT devices to collaboratively train a shared model while preserving the privacy of local data. To avoid the single-point-of-failure of the conventional parameter server architecture, the study concentrates on the decentralized …

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Privacy Preserving Task Push in Spatial Crowdsourcing With Unknown Popularity

Yin Xu, Mingjun Xiao, Jie Wu, Sun He

In this paper, we investigate the privacy-preserving task push problem with unknown popularity in Spatial Crowdsourcing (SC), where the platform needs to select some tasks with unknown popularity and push them to workers. Meanwhile, the preferences of workers and the popularity values …

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Crowdsensing Data Trading for Unknown Market: Privacy, Stability, and Conflicts

Sun He, Mingjun Xiao, Yin Xu, Guoju Gao et autres

In recent years, Crowdsensing Data Trading (CDT) has emerged as a new data trading paradigm, where buyers crowdsource data collection tasks to a group of mobile users with sensing devices (a.k.a., sellers) who sell the collected data to them, through a platform …

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Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach

Yin Xu, Xichong Zhang, Mingjun Xiao, Jie Wu et autres

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic …

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Age-of-Information-Aware Federated Learning

Yin Xu, Mingjun Xiao, Chen Wu, Jie Wu et autres

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Towards Federated Learning on Fresh Datasets

Chen Wu, Mingjun Xiao, Jie Wu, Yin Xu et autres

Federated Learning (FL) is an emerging privacy-preserving distributed computing paradigm that enables numerous clients to collaboratively train machine learning models without the need for transmitting the private datasets of clients to the FL server. Unlike most existing research where the local datasets …

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AoI-Guaranteed Incentive Mechanism for Mobile Crowdsensing With Freshness Concerns

Yin Xu, Mingjun Xiao, Yu Zhu, Jie Wu et autres

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate …

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