Secure Embedding Aggregation for Federated Representation Learning
Author:
Affiliation:
1. The Hong Kong University of Science and Technology
2. The Hong Kong University of Science and Technology,Guangzhou
3. Lehigh University
Funder
Nature
China Postdoctoral Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10206429/10206441/10206974.pdf?arnumber=10206974
Reference37 articles.
1. Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning;so;Proceedings of Machine Learning and Systems,2022
2. New directions in cryptography
3. Fastsecagg: Scalable secure aggregation for privacy-preserving federated learning;kadhe,2020
4. Constant-Round Multi-party Private Set Union Using Reversed Laurent Series
5. SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
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