354 citations · 835 across the 22 of their papers we have counts for
23 papers
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
Hanieh Hashemi, Wenjie Xiong, Liu Ke +4
Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest o…
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train or infer with private data while exploitin…
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
Tiantian Feng, Hanieh Hashemi, Rajat Hebbar +2
Speech emotion recognition (SER) processes speech signals to detect and characterize expressed perceived emotions. Many SER application systems often acquire and transmit speech da…
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Tingting Tang, Ramy E. Ali, Hanieh Hashemi +3
Stragglers, Byzantine workers, and data privacy are the main bottlenecks in distributed cloud computing. Some prior works proposed coded computing strategies to jointly address all…
LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables
Rachit Rajat, Yongqin Wang, Murali Annavaram
Data confidentiality is becoming a significant concern, especially in the cloud computing era. Memory access patterns have been demonstrated to leak critical information such as se…
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
Chaoyang He, Emir Ceyani, Keshav Balasubramanian +2
Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-str…
Byzantine-Robust and Privacy-Preserving Framework for FedML
Hanieh Hashemi, Yongqin Wang, Chuan Guo +1
Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others…
Privacy and Integrity Preserving Training Using Trusted Hardware
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train with private data while exploiting acceler…
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani +11
Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a ma…
Distributed Training of Graph Convolutional Networks using Subgraph Approximation
Alexandra Angerd, Keshav Balasubramanian, Murali Annavaram
Modern machine learning techniques are successfully being adapted to data modeled as graphs. However, many real-world graphs are typically very large and do not fit in memory, ofte…
Secure and Fault Tolerant Decentralized Learning
Saurav Prakash, Hanieh Hashemi, Yongqin Wang +2
Federated learning (FL) is a promising paradigm for training a global model over data distributed across multiple data owners without centralizing clients' raw data. However, shari…
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
Chaoyang He, Murali Annavaram, Salman Avestimehr
Scaling up the convolutional neural network (CNN) size (e.g., width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on…
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsist…
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Protecting the privacy of input data is of growing importance as machine learning methods reach new application domains. In this paper, we provide a unified training and inference…
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search
Chaoyang He, Murali Annavaram, Salman Avestimehr
Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When…
Jupiter: A Networked Computing Architecture
Pradipta Ghosh, Quynh Nguyen, Pranav K Sakulkar +7
In the era of Internet of Things, there is an increasing demand for networked computing to support the requirements of the time-constrained, compute-intensive distributed applicati…
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
Krishna Giri Narra, Zhifeng Lin, Yongqin Wang +2
This work presents Origami, which provides privacy-preserving inference for large deep neural network (DNN) models through a combination of enclave execution, cryptographic blindin…
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Zhifeng Lin, Krishna Giri Narra, Mingchao Yu +2
Training a machine learning model is both compute and data-intensive. Most of the model training is performed on high performance compute nodes and the training data is stored near…
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models
Krishna Narra, Zhifeng Lin, Ganesh Ananthanarayanan +2
Reducing the latency variance in machine learning inference is a key requirement in many applications. Variance is harder to control in a cloud deployment in the presence of stragg…
PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs
Kiran Kumar Matam, Hanieh Hashemi, Murali Annavaram
Graph analytics are at the heart of a broad range of applications such as drug discovery, page ranking, and recommendation systems. When graph size exceeds memory size, out-of-core…
Collage Inference: Using Coded Redundancy for Low Variance Distributed Image Classification
Krishna Giri Narra, Zhifeng Lin, Ganesh Ananthanarayanan +2
MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular. Hosting pre-trained machine learning models in the cloud enables elastic scalabili…
Slack Squeeze Coded Computing for Adaptive Straggler Mitigation
Krishna Giri Narra, Zhifeng Lin, Mehrdad Kiamari +2
While performing distributed computations in today's cloud-based platforms, execution speed variations among compute nodes can significantly reduce the performance and create bottl…
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Mingchao Yu, Zhifeng Lin, Krishna Narra +6
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To allev…