https://cis.temple.edu/~wu/research/publications/Publication_files/A_Personalized_Privacy_Preserving_Mechanism_for_Crowdsourced_Federated_Learning.pdf
The first challenge is how to determine the personalized privacy budget for each worker while mitigating the degree of the global model accuracy degradation incurred by injected parameter perturbations. Each worker wants to choose a smaller privacy budget to enhance its PPL as high as possible. However, the smaller privacy budget means a greater extent of parameter perturbations, which leads ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICDE2024_Online_Federated_Learning_on_Distributed_Unknown_Data_Using_UAVs.pdf
Abstract—Along with the advance of low-altitude economy, a variety of applications based on Unmanned Aerial Vehicles (UAVs) have been developed to accomplish diverse tasks. In this paper, we focus on the scenario of multiple UAVs performing Federated Learning (FL) tasks. Specifically, a group of UAVs is scheduled to repeatedly visit some Points of Interest (PoIs), collect the data produced ...
https://cis.temple.edu/~jiewu/
Jie Wu, Ph.D., Fellow of AAAS, and Fellow of IEEE
https://cis.temple.edu/~wu/research/publications/Publication_files/Wu_LongResume.pdf
A. Srinivasan and Jie Wu, “slackFS - Resilient and Persistent Information Hiding Framework,” ac-cepted to appear in International Journal of Security and Networks (IJSN), Vol. 19, No. 2, 2024, 77-91.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICC2024.pdf
Jie Wu is the Director of the Center for Networked Computing and Laura H. Car-nell professor at Temple University. His current research interests include mobile computing and wireless networks, cloud computing, and network trust and secu-rity. Dr. Wu regularly published in schol-arly journals, conference proceedings, and books.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/jiang_www_2020.pdf
ABSTRACT Serendipity recommendation has attracted more and more atten-tion in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated ...
https://cis.temple.edu/~wu/research/publications/Publication_files/10_bare_jrnl_compsoc.pdf
1.1 Motivation and Challenges Service provisioning and updating refers to the decision-making process of the locations of services within a specific edge computing network to balance the interests of service providers and consumers to achieve the greatest eficiency possible [7]. Numerous mobile devices and sensors in edge networks need to interface with services and exchange data in real-time ...
https://www.fox.temple.edu/sites/fox/files/documents/CVs/xueming-luo-cv.pdf
Bio: Xueming Luo is the Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategy, and Professor of MIS, and Founder/ Director of the Global Institute for Artificial Intelligence & Business Analytics in the Fox School of Business at Temple University. He is an interdisciplinary thought-leader in leveraging AI/ML algorithms, text/audio/image/video big data ...
https://sites.temple.edu/wise2022/files/2022/11/WISE-2022-Schedule-and-Program-11.24.22.pdf
December 14-16, 2022, Copenhagen Business School, Denmark Preliminary – Subject to Change
https://cis.temple.edu/~jiewu/research/publications/Publication_files/WENJUNJIANG2021TWeb.pdf
In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this paper, we study “how to generate a comprehensive review summary from a ...