Peng Sun
Zhejiang University
35 Papers
51 Citations
Peng Sun is an academic researcher from Zhejiang University. The author has contributed to research in topics: Computer science & Compressed sensing. The author has an hindex of 5, co-authored 23 publications. Previous affiliations of Peng Sun include The Chinese University of Hong Kong & University of Tennessee.
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Papers
Pain-FL: Personalized Privacy-Preserving Incentive for Federated Learning
TL;DR: Wang et al. as discussed by the authors proposed a contract-based personalized privacy-preserving incentive for federated learning, named Pain-FL, to provide customized payments for workers with different privacy preferences as compensation for privacy leakage cost.
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Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Mobile Crowdsensing Systems
TL;DR: This paper proposes a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery.
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Sparsest Random Sampling for Cluster-Based Compressive Data Gathering in Wireless Sensor Networks
TL;DR: This paper proposes a sparsest random sampling scheme for cluster-based CDG (SRS-CCDG) in WSNs to achieve energy efficient and robust data collection and proposes analytical models that study the relationship between the size of clusters and the energy cost when using different intra-clusters and inter-cluster transmission schemes.
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Joint User Activity Identification and Channel Estimation for Grant-Free NOMA: A Spatial–Temporal Structure-Enhanced Approach
TL;DR: This article proposes a novel joint user activity identification and channel estimation (JUICE) framework by integrating the temporal correlation of active user sets with multiantenna reception, which could achieve superior user detection performance.
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Shield Against Gradient Leakage Attacks: Adaptive Privacy-Preserving Federated Learning
Jiahui Hu,Zhibo Wang,Yongsheng Shen,Bohan Lin,Peng Sun,Xiaoyi Pang,Jian Liu,Kui Ren +7 more
TL;DR: An Adaptive Privacy-Preserving Federated Learning (Adp-PPFL) framework is proposed to achieve satisfactory privacy protection against GLA, while ensuring good performance in terms of model accuracy and convergence speed.
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