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20132026
most citedMulti-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks

127 citations · 461 across the 65 of their papers we have counts for

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Showing 2021Show all

9 papers · 1 filter

eess.SP2021

Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

Junghoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum +3

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We…

cond-mat.str-el2021★ 4 cited

Induction of Large Magnetic Anisotropy Energy and Formation of Multiple Dirac States in SrIrO Films: Role of correlation and spin-orbit coupling

Amit Chauhan, B. R. K. Nanda

The 5 transition metal oxides, in particular iridates, host novel electronic and magnetic phases due to the interplay between onsite Coulomb repulsion () and spin-orbit coupl…

cs.LG2021★ 1 cited

Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach

Yun-Wei Chu, Elizabeth Tenorio, Laura Cruz +3

We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we speci…

cs.LG2021

Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling

Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2

Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology between t…

cs.LG2021

UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

Su Wang, Seyyedali Hosseinalipour, Maria Gorlatova +2

We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The pr…

eess.SP2021

A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Rajeev Sahay, Christopher G. Brinton, David J. Love

Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into tra…