115 citations · 342 across the 14 of their papers we have counts for
3 papers · 1 filter
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Zechun Liu, Barlas Oguz, Changsheng Zhao +6
Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break d…
Evaluating Privacy Leakage in Split Learning
Xinchi Qiu, Ilias Leontiadis, Luca Melis +2
Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information. In particular, on-device machine learning allows us to avoid sharin…
Green Federated Learning
Ashkan Yousefpour, Shen Guo, Ashish Shenoy +7
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequence, the amount of compute used in trainin…