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https://cis.temple.edu/~wu/research/publications/Publication_files/J-CT-2024-Balancing%20Privacy%20and%20Accuracy%20using%20Siginificant%20Gradient%20Protection%20in%20Federated%20Learning.pdf
Abstract—Previous state-of-the-art studies have demonstrated that adversaries can access sensitive user data by membership inference attacks (MIAs) in Federated Learning (FL). Intro-ducing differential privacy (DP) into the FL framework is an effective way to enhance the privacy of FL. Nevertheless, in differentially private federated learning (DP-FL), local gradients become excessively ...
https://community.mis.temple.edu/mis5203sec951spring2022/files/2019/01/COBIT5-Ver2-enabling.pdf
Disclaimer ISACA has designed this publication, COBIT® 5: Enabling Processes (the ‘Work’), primarily as an educational resource for governance of enterprise IT (GEIT), assurance, risk and security professionals. ISACA makes no claim that use of any of the Work will assure a successful outcome. The Work should not be considered inclusive of all proper information, procedures and tests or ...
https://sites.temple.edu/xifanwu/publications-of-xifan-wu/
Updated on 08/08/2025 81. Kehan Cai, Chunyi Zhang, and Xifan Wu, “Simulations of dielectric permittivity of water by Machine Learned Potentials with long-range Coulombic interactions “, Submitted (PDF). 80. Tiancheng Liang, Liying Zhou, Yizhi Song, Xifan Wu, and Limei Xu, “Ion-specific hydration structures revealed by SCAN-based ab initio simulations “, Submitted (2025). 79. Yizhi Song ...
https://cphapps.temple.edu/wiki/it/apps/redcap
REDCap REDCap is a secure web application for building and managing online surveys and databases. While REDCap can be used to collect virtually any type of data in any environment (including compliance with 21 CFR Part 11, FISMA, HIPAA, and GDPR), it is specifically geared to support online and offline data capture for research studies and operations. Access REDCap Join the REDCap Community on ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/INFOCOM2024_PSFL%20Parallel-Sequential%20Federated%20Learning%20with%20Convergence%20Guarantees.pdf
Abstract—Federated Learning (FL) is a novel distributed learning paradigm which can coordinate multiple clients to jointly train a machine learning model by using their local data samples. Existing FL works can be roughly divided into two categories according to the modes of model training: Parallel FL (PFL) and Sequential FL (SFL). PFL can speed up each round of model training time through ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Distributed_Deep_Multi-Agent_Reinforcement_Learning_for_Cooperative_Edge_Caching_in_Internet-of-Vehicles.pdf
Abstract—Edge caching is a promising approach to reduce duplicate content transmission in Internet-of-Vehicles (IoVs). Sev-eral Reinforcement Learning (RL) based edge caching methods have been proposed to improve the resource utilization and reduce the backhaul trafic load. However, they only obtain the local sub-optimal solution, as they neglect the influence from environments by other ...
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https://sites.temple.edu/ticlj/files/2025/09/08_36TempIntlCompLJ1512021-2022.pdf
Despite its huge potential, battery storage fits awkwardly within the siloed frameworks of restructured power markets that separately regulate electricity generation, transmission, and distribution functions under entirely different rules and procedures. Regulatory uncertainty could lead to chronic underinvestment at the peril of a sustainable grid. Lingering questions about how, where, and ...