no code implementations • 12 Apr 2024 • Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, G. Edward Suh, Minsoo Rhu
Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection.
no code implementations • 9 Sep 2023 • 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.
no code implementations • 5 Jun 2023 • Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong, Kiwan Maeng, Hsien-Hsin S. Lee, G. Edward Suh
This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data.
no code implementations • 26 Mar 2023 • Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael Rabbat, Carole-Jean Wu, Ilya Mironov
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets.
1 code implementation • 26 Jan 2023 • Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Yang Li, Liangzhen Lai, Ilias Leontiadis, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
no code implementations • 12 Dec 2022 • Hanieh Hashemi, Wenjie Xiong, Liu Ke, Kiwan Maeng, Murali Annavaram, G. Edward Suh, Hsien-Hsin S. Lee
This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns.
no code implementations • 21 Sep 2022 • Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, Edward Suh
Split learning and inference propose to run training/inference of a large model that is split across client devices and the cloud.
no code implementations • 12 Sep 2022 • Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K Qureshi, Hsien-Hsin S. Lee
Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners.
no code implementations • 7 Jun 2022 • Meisam Hejazinia Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu
Despite FL's initial success, many important deep learning use cases, such as ranking and recommendation tasks, have been limited from on-device learning.
no code implementations • 30 May 2022 • Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device.
1 code implementation • 30 Oct 2021 • Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware.
no code implementations • 5 Nov 2020 • Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu
The paper is the first to the extent of our knowledge to perform a data-driven, in-depth analysis of applying partial recovery to recommendation models and identified a trade-off between accuracy and performance.
no code implementations • 4 Dec 2019 • Kiwan Maeng, Iskender Kushan, Brandon Lucia, Ashish Kapoor
We propose a framework to collect stratospheric data by releasing a contrail of tiny sensor devices as a weather balloon ascends.