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...
share
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...
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...
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...
research
∙
04/24/2023
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation
Heterogeneity across devices in federated learning (FL) typically refers...
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...
research
∙
03/22/2023
Delay-Aware Hierarchical Federated Learning
Federated learning has gained popularity as a means of training models d...
research
∙
03/15/2023
Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks
Semi-decentralized federated learning blends the conventional device to-...
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...
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...
research
∙
11/23/2022
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices
Federated learning (FL) is a technique for distributed machine learning ...
research
∙
10/29/2022
Linear Coding for Gaussian Two-Way Channels
We consider linear coding for Gaussian two-way channels (GTWCs), in whic...
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...
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...
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...
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...
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...
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...
research
∙
05/07/2022
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms...
research
∙
04/07/2022
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds
A recent emphasis of distributed learning research has been on federated...
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)....
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...
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...
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...
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...
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...
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...
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...
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...
research
∙
01/04/2021
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
The conventional federated learning (FedL) architecture distributes mach...
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...
research
∙
10/05/2020
Towards Generalized and Distributed Privacy-Preserving Representation Learning
We study the problem of learning data representations that are private y...
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...
research
∙
07/26/2020
Fast-Convergent Federated Learning
Federated learning has emerged recently as a promising solution for dist...
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...
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 ...
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...
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...
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...
research
∙
09/26/2019
Dynamic Interference Management for UAV-Assisted Wireless Networks
The deployment of unmanned aerial vehicles (UAVs) is proliferating as th...
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...
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...
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...
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...
research
∙
03/07/2019
Allocation of Computation-Intensive Graph Jobs over Vehicular Clouds
Recent years have witnessed dramatic growth in smart vehicles and comput...
research
∙
03/04/2019
Interference Avoidance Position Planning in UAV-assisted Wireless Communication
We consider unmanned aerial vehicle (UAV)-assisted wireless communicatio...
research
∙
08/13/2018
Allocation of Graph Jobs in Geo-Distributed Cloud Networks
Recently, processing of big-data has drawn tremendous attention, where c...
research
∙
07/11/2018