Privacy Preserving Through Federated Learning

Gokul K. Sunil; C. U. Om Kumar; R. Krithiga; M Suguna; M. Revathi

doi:10.1007/978-981-99-4577-1_24

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Privacy Preserving Through Federated Learning

Gokul K. Sunil C. U. Om Kumar R. Krithiga M Suguna M. Revathi

Year: 2023 Algorithms for intelligent systems Pages: 295-307 Publisher: Springer Nature

DOI: 10.1007/978-981-99-4577-1_24

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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

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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

Privacy Preserving Through Federated Learning

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