A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward
Author:
Affiliation:
1. Department of Computer Engineering, Gachon University, Seongnam, South Korea
Funder
National Research Foundation of Korea
Korea Government
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10555253.pdf?arnumber=10555253
Reference492 articles.
1. Federated vs. Centralized Machine Learning under Privacy-elastic Users: A Comparative Analysis
2. Federated learning: Collaborative machine learning without centralized training data;McMahan;Google Res. Blog,2017
3. Federated Learning in Vehicular Networks
4. Federated Learning for Smart Healthcare: A Survey
5. A survey on federated learning
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