Seyyedali Hosseinalipour

research

∙ 07/28/2023

DISCO: Achieving Low Latency and High Reliability in Scheduling of Graph-Structured Tasks over Mobile Vehicular Cloud

To effectively process data across a fleet of dynamic and distributed ve...

0 Minghui LiWang, et al. ∙

research

∙ 07/03/2023

GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds

Vehicular clouds (VCs) are modern platforms for processing of computatio...

0 Zhang Liu, et al. ∙

research

∙ 06/25/2023

Matching-based Hybrid Service Trading for Task Assignment over Dynamic Mobile Crowdsensing Networks

By opportunistically engaging mobile users (workers), mobile crowdsensin...

0 Houyi Qi, et al. ∙

research

∙ 05/22/2023

Asynchronous Multi-Model Federated Learning over Wireless Networks: Theory, Modeling, and Optimization

Federated learning (FL) has emerged as a key technique for distributed m...

0 Zhan-Lun Chang, et al. ∙

research

∙ 04/24/2023

Multi-Source to Multi-Target Decentralized Federated Domain Adaptation

Heterogeneity across devices in federated learning (FL) typically refers...

0 Su Wang, et al. ∙

research

∙ 04/14/2023

Synergies Between Federated Learning and O-RAN: Towards an Elastic Virtualized Architecture for Multiple Distributed Machine Learning Services

Federated learning (FL) is the most popular distributed machine learning...

0 Payam Abdisarabshali, et al. ∙

research

∙ 03/22/2023

Delay-Aware Hierarchical Federated Learning

Federated learning has gained popularity as a means of training models d...

0 Frank Po-Chen Lin, et al. ∙

research

∙ 03/15/2023

Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks

Semi-decentralized federated learning blends the conventional device to-...

0 Rohit Parasnis, et al. ∙

research

∙ 03/15/2023

Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks

Federated learning (FL) has been promoted as a popular technique for tra...

0 Su Wang, et al. ∙

research

∙ 12/05/2022

Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics

Traditional learning-based approaches to student modeling (e.g., predict...

0 Yun-Wei Chu, et al. ∙

research

∙ 11/23/2022

Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices

Federated learning (FL) is a technique for distributed machine learning ...

0 Shahryar Zehtabi, et al. ∙

research

∙ 10/29/2022

Linear Coding for Gaussian Two-Way Channels

We consider linear coding for Gaussian two-way channels (GTWCs), in whic...

0 Junghoon Kim, et al. ∙

research

∙ 10/13/2022

Decomposition Theory Meets Reliability Analysis: Processing of Computation-Intensive Dependent Tasks over Vehicular Clouds with Dynamic Resources

Vehicular cloud (VC) is a promising technology for processing computatio...

0 Payam Abdisarabshali, et al. ∙

research

∙ 10/10/2022

Seamless Service Provisioning for Mobile Crowdsensing: Towards Integrating Forward and Spot Trading Markets

The challenge of exchanging and processing of big data over Mobile Crowd...

0 Minghui LiWang, et al. ∙

research

∙ 08/26/2022

RFID: Towards Low Latency and Reliable DAG Task Scheduling over Dynamic Vehicular Clouds

Vehicular cloud (VC) platforms integrate heterogeneous and distributed r...

0 Zhang Liu, et al. ∙

research

∙ 08/04/2022

Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange

Federated learning (FL) has been recognized as one of the most promising...

3 Satyavrat Wagle, et al. ∙

research

∙ 08/02/2022

Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning

Traditional learning-based approaches to student modeling generalize poo...

0 Yun-Wei Chu, et al. ∙

research

∙ 06/09/2022

Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and Computing

Mobile crowd sensing and computing (MCSC) enables heterogeneous users (w...

0 Minghui LiWang, et al. ∙

research

∙ 05/07/2022

Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms...

0 Junghoon Kim, et al. ∙

research

∙ 04/07/2022

Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds

A recent emphasis of distributed learning research has been on federated...

0 Shahryar Zehtabi, et al. ∙

research

∙ 03/26/2022

Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point

We propose cooperative edge-assisted dynamic federated learning (CE-FL)....

0 Bhargav Ganguly, et al. ∙

research

∙ 03/18/2022

Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing

In this paper, we study a new latency optimization problem for Blockchai...

0 Dinh C Nguyen, et al. ∙

research

∙ 02/07/2022

Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks

Federated learning (FedL) has emerged as a popular technique for distrib...

3 Seyyedali Hosseinalipour, et al. ∙

research

∙ 02/01/2022

Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?

In this paper, we question the rationale behind propagating large number...

0 Sheikh Shams Azam, et al. ∙

research

∙ 12/27/2021

Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity

Federated learning (FL) has emerged as a popular technique for distribut...

0 David Nickel, et al. ∙

research

∙ 09/07/2021

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

Federated learning has emerged as a popular technique for distributing m...

0 Frank Po-Chen Lin, et al. ∙

research

∙ 06/29/2021

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

We consider distributed machine learning (ML) through unmanned aerial ve...

33 Su Wang, et al. ∙

research

∙ 03/18/2021

Two Timescale Hybrid Federated Learning with Cooperative D2D Local Model Aggregations

Federated learning has emerged as a popular technique for distributing m...

16 Frank Po-Chen Lin, et al. ∙

research

∙ 01/25/2021

Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach

Reliable communication through multiple-input multiple-output (MIMO) ort...

0 Myeung Suk Oh, et al. ∙

research

∙ 01/04/2021

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

The conventional federated learning (FedL) architecture distributes mach...

0 Su Wang, et al. ∙

research

∙ 11/02/2020

Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach

Applications of intelligent reflecting surfaces (IRSs) in wireless netwo...

0 Junghoon Kim, et al. ∙

research

∙ 10/05/2020

Towards Generalized and Distributed Privacy-Preserving Representation Learning

We study the problem of learning data representations that are private y...

0 Sheikh Shams Azam, et al. ∙

research

∙ 09/29/2020

A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions

The combinatorial auction (CA) is an efficient mechanism for resource al...

0 Mengyuan Lee, et al. ∙

research

∙ 07/26/2020

Fast-Convergent Federated Learning

Federated learning has emerged recently as a promising solution for dist...

0 Hung T. Nguyen, et al. ∙

research

∙ 07/18/2020

Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks

One of the popular methods for distributed machine learning (ML) is fede...

0 Seyyedali Hosseinalipour, et al. ∙

research

∙ 06/07/2020

From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks

Contemporary network architectures are pushing computing tasks from the ...

0 Seyyedali Hosseinalipour, et al. ∙

research

∙ 04/04/2020

Energy-aware Allocation of Graph Jobs in Vehicular Cloud Computing-enabled Software-defined IoV

Software-defined internet of vehicles (SDIoV) has emerged as a promising...

0 Minghui LiWang, et al. ∙

research

∙ 03/27/2020

A Truthful Auction for Graph Job Allocation in Vehicular Cloud-assisted Networks

Vehicular cloud computing has emerged as a promising solution to fulfill...

0 Zhibin Gao, et al. ∙

research

∙ 12/12/2019

Multi-Task Offloading over Vehicular Clouds under Graph-based Representation

Vehicular cloud computing has emerged as a promising paradigm for realiz...

0 Minghui LiWang, et al. ∙

research

∙ 09/26/2019

Dynamic Interference Management for UAV-Assisted Wireless Networks

The deployment of unmanned aerial vehicles (UAVs) is proliferating as th...

0 Ali Rahmati, et al. ∙

research

∙ 07/02/2019

Interference Avoidance Position Planning in Dual-hop and Multi-hop UAV Relay Networks

We consider unmanned aerial vehicle (UAV)-assisted wireless communicatio...

0 Seyyedali Hosseinalipour, et al. ∙

research

∙ 04/16/2019

Interference Avoidance in UAV-Assisted Networks: Joint 3D Trajectory Design and Power Allocation

The use of the unmanned aerial vehicle (UAV) has been foreseen as a prom...

0 Ali Rahmati, et al. ∙

research

∙ 03/21/2019

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

We propose a generic system model for a special category of interdepende...

0 Seyyedali Hosseinalipour, et al. ∙

research

∙ 03/11/2019

Optimal Time Allocation in VANETs Advertising: A Price-based Approach using Stackelberg Game

Vehicular ad-hoc networks (VANETs) have recently attracted a lot of atte...

0 Ali Rahmati, et al. ∙

research

∙ 03/07/2019

Allocation of Computation-Intensive Graph Jobs over Vehicular Clouds

Recent years have witnessed dramatic growth in smart vehicles and comput...

0 Minghui LiWang, et al. ∙

research

∙ 03/04/2019

Interference Avoidance Position Planning in UAV-assisted Wireless Communication

We consider unmanned aerial vehicle (UAV)-assisted wireless communicatio...

0 Seyyedali Hosseinalipour, et al. ∙

research

∙ 08/13/2018

Allocation of Graph Jobs in Geo-Distributed Cloud Networks

Recently, processing of big-data has drawn tremendous attention, where c...

0 Seyyedali Hosseinalipour, et al. ∙

research

∙ 07/11/2018

A Two-Stage Auction Mechanism for Cloud Resource Allocation

With the recent growth in the size of cloud computing business, handling...

0 Seyyedali Hosseinalipour, et al. ∙

Seyyedali Hosseinalipour

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