Differentially private federated learning with non-IID data

Shuyan Cheng; Peng Li; Ruchuan Wang; He Xu

doi:10.1007/s00607-024-01257-2

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Differentially private federated learning with non-IID data

Shuyan Cheng Peng Li Ruchuan Wang He Xu

Year: 2024 Journal: Computing Vol: 106 (7)Pages: 2459-2488 Publisher: Springer Science+Business Media

DOI: 10.1007/s00607-024-01257-2

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Keywords:

Differential privacy Computer science Convergence (economics) Code (set theory) Noise (video) Clipping (morphology) Differential (mechanical device) Information privacy Data mining Algorithm Artificial intelligence Computer security

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Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Stochastic Gradient Optimization Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Cryptography and Data Security

Physical Sciences → Computer Science → Artificial Intelligence

Differentially private federated learning with non-IID data

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