research
∙
12/12/2022
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
Online personalized recommendation services are generally hosted in the ...
share
research
∙
09/27/2022
MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference
Multi-party computing (MPC) has been gaining popularity over the past ye...
research
∙
06/30/2022
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware
Privacy and security-related concerns are growing as machine learning re...
research
∙
12/26/2021
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
Speech emotion recognition (SER) processes speech signals to detect and ...
research
∙
07/27/2021
Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Stragglers, Byzantine workers, and data privacy are the main bottlenecks...
research
∙
07/16/2021
Look Ahead ORAM: Obfuscating Addresses in Recommendation Model Training
In the cloud computing era, data privacy is a critical concern. Memory a...
research
∙
06/04/2021
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
Graph Neural Networks (GNNs) are the first choice methods for graph mach...
research
∙
05/05/2021
Byzantine-Robust and Privacy-Preserving Framework for FedML
Federated learning has emerged as a popular paradigm for collaboratively...
research
∙
05/01/2021
Privacy and Integrity Preserving Training Using Trusted Hardware
Privacy and security-related concerns are growing as machine learning re...
research
∙
04/14/2021
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Graph Neural Network (GNN) research is rapidly growing thanks to the cap...
research
∙
12/09/2020
Distributed Training of Graph Convolutional Networks using Subgraph Approximation
Modern machine learning techniques are successfully being adapted to dat...
research
∙
10/17/2020
Check-N-Run: A Checkpointing System for Training Recommendation Models
Checkpoints play an important role in training recommendation systems at...
research
∙
07/28/2020
Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge
Scaling up the convolutional neural network (CNN) size (e.g., width, dep...
research
∙
06/01/2020
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks
Protecting the privacy of input data is of growing importance as machine...
research
∙
04/18/2020
FedNAS: Federated Deep Learning via Neural Architecture Search
Federated Learning (FL) has been proved to be an effective learning fram...
research
∙
12/23/2019
Jupiter: A Networked Computing Architecture
In the era of Internet of Things, there is an increasing demand for netw...
research
∙
12/07/2019
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
This work presents Origami, which provides privacy-preserving inference ...
research
∙
10/22/2019
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Training a machine learning model is both compute and data-intensive. Mo...
research
∙
06/05/2019
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models
Reducing the latency variance in machine learning inference is a key req...
research
∙
05/10/2019
PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs
Graphs analytics are at the heart of a broad range of applications such ...
research
∙
04/27/2019
Collage Inference: Tolerating Stragglers in Distributed Neural Network Inference using Coding
MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becom...
research
∙
04/15/2019
Distributed Matrix Multiplication Using Speed Adaptive Coding
While performing distributed computations in today's cloud-based platfor...
research
∙
11/08/2018
