13 citations · 39 across the 17 of their papers we have counts for
7 papers · 1 filter
Beyond Prediction: Tail-Aware Scheduling for LLM Inference
Yueying Li, Yuanfan Chen, Jiayang Chen +6
LLM serving exhibits extreme length variability, making size-based scheduling difficult in practice. Recent LLM schedulers approximate SJF/SRPT using predicted decode lengths or ra…
A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Models
Trishita Tiwari, Ari Trachtenberg, G. Edward Suh
Training data leakage from Large Language Models (LLMs) raises serious concerns related to privacy, security, and copyright compliance. A central challenge in assessing this risk i…
Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference
Kiwan Maeng, G. Edward Suh
Secure multi-party computation (MPC) allows users to offload machine learning inference on untrusted servers without having to share their privacy-sensitive data. Despite their str…
Information Flow Control in Machine Learning through Modular Model Architecture
Trishita Tiwari, Suchin Gururangan, Chuan Guo +7
In today's machine learning (ML) models, any part of the training data can affect the model output. This lack of control for information flow from training data to model output is…
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
Kiwan Maeng, Chuan Guo, Sanjay Kariyappa +1
Privacy-preserving instance encoding aims to encode raw data as feature vectors without revealing their privacy-sensitive information. When designed properly, these encodings can b…
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
Sanjay Kariyappa, Chuan Guo, Kiwan Maeng +4
Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners. To this end, FL requires the data owners to perform tr…