Mohammad Naseri

About Mohammad Naseri

Mohammad Naseri, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at University College London, specializes in the field of Privacy, Security, Trustworthy Machine Learning, Federated Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection

Buffered Asynchronous Federated Learning with Local Differential Privacy

Bayesian estimation of differential privacy

Badvfl: Backdoor attacks in vertical federated learning

Cerberus: Exploring Federated Prediction of Security Events

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Mohammad Naseri Information

University

Position

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Citations(all)

207

Citations(since 2020)

207

Cited By

21

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

Google Scholar

Mohammad Naseri Skills & Research Interests

Privacy

Security

Trustworthy Machine Learning

Federated Learning

Top articles of Mohammad Naseri

Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection

arXiv preprint arXiv:2401.10765

2024/1/19

Buffered Asynchronous Federated Learning with Local Differential Privacy

2023/10/13

Mohammad Naseri
Mohammad Naseri

H-Index: 2

Emiliano De Cristofaro
Emiliano De Cristofaro

H-Index: 38

Bayesian estimation of differential privacy

2023/7/3

Badvfl: Backdoor attacks in vertical federated learning

arXiv preprint arXiv:2304.08847

2023/4/18

Mohammad Naseri
Mohammad Naseri

H-Index: 2

Emiliano De Cristofaro
Emiliano De Cristofaro

H-Index: 38

Cerberus: Exploring Federated Prediction of Security Events

The 29th ACM Conference on Computer and Communications Security (CCS) 2022

2022/9/7

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

2022

See List of Professors in Mohammad Naseri University(University College London)

Co-Authors

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