research
∙
08/21/2023
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Machine learning (ML) is widely used for key tasks in Connected and Auto...
share
research
∙
08/08/2023
Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Existing approaches to distributed matrix computations involve allocatin...
research
∙
07/20/2023
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
This paper proposes a novel communication-efficient split learning (SL) ...
research
∙
07/09/2023
Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II
We revisit the binary adversarial wiretap channel (AWTC) of type II in w...
research
∙
07/07/2023
On Pseudolinear Codes for Correcting Adversarial Errors
We consider error-correction coding schemes for adversarial wiretap chan...
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
∙
04/20/2023
On the Effects of Data Heterogeneity on the Convergence Rates of Distributed Linear System Solvers
We consider the fundamental problem of solving a large-scale system of l...
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
∙
02/23/2023
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Federated learning (FL) is a popular technique for training a global mod...
research
∙
02/04/2023
Cross-Layer Federated Learning Optimization in MIMO Networks
In this paper, the performance optimization of federated learning (FL), ...
research
∙
01/30/2023
Distributed Matrix Computations with Low-weight Encodings
Straggler nodes are well-known bottlenecks of distributed matrix computa...
research
∙
12/16/2022
SplitGP: Achieving Both Generalization and Personalization in Federated Learning
A fundamental challenge to providing edge-AI services is the need for a ...
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
∙
09/21/2022
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
This paper considers improving wireless communication and computation ef...
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
∙
07/25/2022
AGAPECert: An Auditable, Generalized, Automated, Privacy-Enabling Certification Framework with Oblivious Smart Contracts
This paper introduces AGAPECert, an Auditable, Generalized, Automated, P...
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
∙
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
∙
12/02/2020
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Despite the recent successes of deep learning in natural language proces...
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
∙
11/02/2020
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Automatic modulation classification (AMC) aims to improve the efficiency...
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
∙
08/21/2020
Federated Learning with Communication Delay in Edge Networks
Federated learning has received significant attention as a potential sol...
research
∙
08/05/2020
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
Existing topic modeling and text segmentation methodologies generally re...
research
∙
07/26/2020
Fast-Convergent Federated Learning
Federated learning has emerged recently as a promising solution for dist...
research
∙
07/25/2020
Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Device-to-device (D2D) communications is expected to be a critical enabl...
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
∙
04/17/2020
Network-Aware Optimization of Distributed Learning for Fog Computing
Fog computing promises to enable machine learning tasks to scale to larg...
research
∙
02/27/2020
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
In this paper, we study the distributed computational capabilities of de...
research
∙
01/23/2020
A Deep Learning Approach to Behavior-Based Learner Modeling
The increasing popularity of e-learning has created demand for improving...
research
∙
06/22/2018