Privacy-Preserving Mobility-Aware Federated Collaborative Filtering Framework for Caching Prediction in Vehicular Networks-Reference-Cited by-同舟云学术

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Privacy-Preserving Mobility-Aware Federated Collaborative Filtering Framework for Caching Prediction in Vehicular Networks

Published:2023-09-11 Issue: Volume: Page:
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Container-title:2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
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Author:

Ouyang Xinzhi¹,Feng Chenyuan²,Feng Daquan²,Yang Howard H.³

Affiliation:

1. State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen,China,518055

2. Shenzhen Key Laboratory of Digital Creative Technology, Shenzhen University,Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication,Shenzhen,China,518060

3. Zhejiang University/University of Illinois at Urbana-Champaign Institute, Zhejiang University,Haining,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10287388/10287413/10287533.pdf?arnumber=10287533

Reference16 articles.

1. The Algorithmic Foundations of Differential Privacy

2. Scalable collaborative filtering approaches for large recommender systems;takacs;The Journal of Machine Learning Research,2009

3. Federated collaborative filtering for privacy-preserving personalized recommendation system;ammad-ud-din;arXiv preprint arXiv 1901 09094,2019

4. Differentially Private Matrix Factorization using Sketching Techniques

5. Large-scale matrix factorization with distributed stochastic gradient descent

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