BOOK-CHAPTER

Privacy Preserving Through Federated Learning

Keywords:
Paillier cryptosystem Computer science Homomorphic encryption MNIST database Encryption Scheme (mathematics) Overhead (engineering) Process (computing) Artificial intelligence Machine learning Field (mathematics) Cryptosystem Computer security Deep learning Hybrid cryptosystem Operating system

Metrics

1
Cited By
0.65
FWCI (Field Weighted Citation Impact)
21
Refs
0.69
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Privacy-Preserving Technologies in Data
Physical Sciences →  Computer Science →  Artificial Intelligence
Cryptography and Data Security
Physical Sciences →  Computer Science →  Artificial Intelligence
Stochastic Gradient Optimization Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

Related Documents

JOURNAL ARTICLE

Privacy-Preserving AI Through Federated Learning

Pushkar Mehendale

Journal:   Zenodo (CERN European Organization for Nuclear Research) Year: 2021
JOURNAL ARTICLE

Privacy-Preserving AI Through Federated Learning

Pushkar Mehendale

Journal:   Zenodo (CERN European Organization for Nuclear Research) Year: 2021
BOOK-CHAPTER

Privacy-Preserving Federated Learning

Kwangjo KimHarry Chandra Tanuwidjaja

SpringerBriefs on cyber security systems and networks Year: 2021 Pages: 55-63
JOURNAL ARTICLE

Enhancing Privacy-Preserving Intrusion Detection through Federated Learning

Jan, Tony

Journal:   OPAL (Open@LaTrobe) (La Trobe University) Year: 2025
© 2026 ScienceGate Book Chapters — All rights reserved.