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20232026
most citedOptimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis

2 citations · 3 across the 12 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.LG2024

AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning

Shuaijun Chen, Omid Tavallaie, Niousha Nazemi +2

As data volumes expand rapidly, distributed machine learning has become essential for addressing the growing computational demands of modern AI systems. However, training models in…

cs.LG2024

RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS

Shuaijun Chen, Omid Tavallaie, Niousha Nazemi +1

Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped w…

cs.LG2024★ 1 cited

SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks

Omid Tavallaie, Kanchana Thilakarathna, Suranga Seneviratne +2

Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Ind…

cs.CR2024

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…

cs.NI2024

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…

cs.DC2024

Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models

Wentao Gao, Omid Tavallaie, Shuaijun Chen +1

Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without shari…