EAPS: Edge-Assisted Privacy-Preserving Federated Prediction Systems-Reference-Cited by-同舟云学术

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EAPS: Edge-Assisted Privacy-Preserving Federated Prediction Systems

Published:2023-03 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Wireless Communications and Networking Conference (WCNC)
language:
Short-container-title:

Author:

Feng Daquan¹,Huang Guanxin¹,Feng Chenyuan¹,Cao Bin²,Wang Zhenzhong³,Xia Xiang-Gen⁴

Affiliation:

1. Shenzhen University,Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication,Shenzhen,China

2. Beijing University of Posts and Telecommunications,State Key Laboratory of Networking and Switching Technology,Beijing,China

3. China Media Group,Technical Management Center,Beijing,China

4. University of Delaware,Department of Electrical and Computer Engineering,Newark,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10118584/10118587/10118906.pdf?arnumber=10118906

Reference12 articles.

1. Neural Collaborative Filtering

2. Robust federated recommendation system;chen,2020

3. Personalized federated learning: A meta-learning approach;fallah,2020

4. User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

5. Federated Recommendation System via Differential Privacy

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust Privacy-Preserving Recommendation Systems Driven by Multimodal Federated Learning;IEEE Transactions on Neural Networks and Learning Systems;2024

2. Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems;IEEE Transactions on Communications;2023-12

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