Y
Yanmin Gong
Researcher at University of Texas at San Antonio
Publications - 61
Citations - 1437
Yanmin Gong is an academic researcher from University of Texas at San Antonio. The author has contributed to research in topics: Differential privacy & Computer science. The author has an hindex of 13, co-authored 50 publications receiving 771 citations. Previous affiliations of Yanmin Gong include University of Texas at Austin & Oklahoma State University–Stillwater.
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Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing
TL;DR: An innovative UAV-enabled MEC system involving the interactions among IoT devices, UAV, and edge clouds (ECs) and an efficient algorithm based on the successive convex approximation to obtain suboptimal solutions is proposed.
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A Privacy-Preserving Scheme for Incentive-Based Demand Response in the Smart Grid
TL;DR: This paper proposes a privacy-preserving scheme for IDR programs in the smart grid, which enables the DR provider to compute individual demand curtailments and DR rewards while preserving customer privacy.
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Personalized Federated Learning With Differential Privacy
TL;DR: A privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data is proposed and the experimental results demonstrate that the proposed approach is robust to user heterogeneity and offers a good tradeoff between accuracy and privacy.
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Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage
TL;DR: An online control algorithm based on the Lyapunov optimization technique, called Stochastic Cost Minimization Algorithm (SCMA), is proposed to solve the problem of green energy integration and reduce the cost of brown energy usage.
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DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
TL;DR: This paper proposes a novel differentially private ADMM-based distributed learning algorithm called DP-ADMM, which combines an approximate augmented Lagrangian function with time-varying Gaussian noise addition in the iterative process to achieve higher utility for general objective functions under the same differential privacy guarantee.