Christopher G. Brinton

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...

0 Vishnu Pandi Chellapandi, et al. ∙

research

∙ 08/08/2023

Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations

Existing approaches to distributed matrix computations involve allocatin...

0 Anindya Bijoy Das, et al. ∙

research

∙ 07/20/2023

Communication-Efficient Split Learning via Adaptive Feature-Wise Compression

This paper proposes a novel communication-efficient split learning (SL) ...

0 Yongjeong Oh, et al. ∙

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...

0 Eric Ruzomberka, et al. ∙

research

∙ 07/07/2023

On Pseudolinear Codes for Correcting Adversarial Errors

We consider error-correction coding schemes for adversarial wiretap chan...

0 Eric Ruzomberka, 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

∙ 04/20/2023

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...

0 Boris Velasevic, 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

∙ 02/23/2023

Coded Matrix Computations for D2D-enabled Linearized Federated Learning

Federated learning (FL) is a popular technique for training a global mod...

0 Anindya Bijoy Das, et al. ∙

research

∙ 02/04/2023

Cross-Layer Federated Learning Optimization in MIMO Networks

In this paper, the performance optimization of federated learning (FL), ...

0 Sihua Wang, et al. ∙

research

∙ 01/30/2023

Distributed Matrix Computations with Low-weight Encodings

Straggler nodes are well-known bottlenecks of distributed matrix computa...

0 Anindya Bijoy Das, et al. ∙

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 ...

0 Dong-Jun Han, 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

∙ 09/21/2022

Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

This paper considers improving wireless communication and computation ef...

0 Sihua Wang, 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

∙ 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...

0 Servio Palacios, 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

∙ 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

∙ 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...

0 Adam Hare, 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

∙ 11/02/2020

Frequency-based Automated Modulation Classification in the Presence of Adversaries

Automatic modulation classification (AMC) aims to improve the efficiency...

0 Rajeev Sahay, 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

∙ 08/21/2020

Federated Learning with Communication Delay in Edge Networks

Federated learning has received significant attention as a potential sol...

0 Frank Po-Chen Lin, et al. ∙

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...

0 Sirui Wang, 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

∙ 07/25/2020

Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks

Device-to-device (D2D) communications is expected to be a critical enabl...

0 Junghoon Kim, 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

∙ 04/17/2020

Network-Aware Optimization of Distributed Learning for Fog Computing

Fog computing promises to enable machine learning tasks to scale to larg...

0 Yuwei Tu, et al. ∙

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...

0 Junghoon Kim, et al. ∙

research

∙ 01/23/2020

A Deep Learning Approach to Behavior-Based Learner Modeling

The increasing popularity of e-learning has created demand for improving...

0 Yuwei Tu, et al. ∙

research

∙ 06/22/2018

Personalized Thread Recommendation for MOOC Discussion Forums

Social learning, i.e., students learning from each other through social ...

0 Andrew S. Lan, et al. ∙

Christopher G. Brinton

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