354 citations · 835 across the 22 of their papers we have counts for
7 papers · 1 filter
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…