Adaptive Federated Learning for Non-Convex Optimization Problems in Edge Computing Environment-Reference-Cited by-同舟云学术

Adaptive Federated Learning for Non-Convex Optimization Problems in Edge Computing Environment

Published:2022-09-01 Issue:5 Volume:9 Page:3478-3491
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Qiao Dewen¹,Liu Guiyan¹^ORCID,Guo Songtao¹^ORCID,He Jing¹^ORCID

Affiliation:

1. Key Laboratory of Dependable Service Computing in Cyber-Physical-Society (Ministry of Education), college of computer science, Chongqing University, Chongqing, Shapingba District, China

Funder

Natural Science Key Foundation of Chongqing

China Postdoctoral Science Foundation

Chongqing Natural Science Foundation for Postdoctoral

Fundamental Research Funds for the Central Universities

Zhejiang Lab

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/6488902/9882997/09802924.pdf?arnumber=9802924

Reference43 articles.

1. Non-convex Optimization for Machine Learning

2. What is the expectation maximization algorithm?

3. Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering

4. Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT Edge Devices

5. Energy-Aware Concurrent Data Aggregation Scheduling for Wireless Powered IoT Leveraging Hypergraph Theory

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