16 citations · 18 across the 5 of their papers we have counts for
5 papers
ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks
Niousha Nazemi, Omid Tavallaie, Shuaijun Chen +5
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data…
Analysis of DNS Dependencies and their Security Implications in Australia: A Comparative Study of General and Indigenous Populations
Niousha Nazemi, Omid Tavallaie, Anna Maria Mandalari +3
This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We…
Feedback to the European Data Protection Board's Guidelines 2/2023 on Technical Scope of Art. 5(3) of ePrivacy Directive
Cristiana Santos, Nataliia Bielova, Vincent Roca +4
We very much welcome the EDPB's Guidelines. Please find hereunder our feedback to the Guidelines 2/2023 on Technical Scope of Art. 5(3) of ePrivacy Directive. Our comments are pres…
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Siqi Li, Di Miao, Qiming Wu +9
Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering commun…
Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
Shuaijun Chen, Omid Tavallaie, Michael Henri Hambali +5
Federated learning (FL) is a novel distributed learning framework designed for applications with privacy-sensitive data. Without sharing data, FL trains local models on individual…