no code implementations • 11 Sep 2024 • Khiem Ton, Nhi Nguyen, Mahmoud Nazzal, Abdallah Khreishah, Cristian Borcea, NhatHai Phan, Ruoming Jin, Issa Khalil, Yelong Shen
This paper introduces SGCode, a flexible prompt-optimizing system to generate secure code with large language models (LLMs).
no code implementations • 12 Nov 2023 • Xiaopeng Jiang, Cristian Borcea
To avoid interference among client models with different concepts, the server clusters the models representing the same concept, aggregates the model weights in each cluster, and updates the global concept model with the cluster model of the same concept.
no code implementations • 10 Mar 2023 • Xiaopeng Jiang, Thinh On, NhatHai Phan, Hessamaldin Mohammadi, Vijaya Datta Mayyuri, An Chen, Ruoming Jin, Cristian Borcea
However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users increases, and protects user data privacy.
no code implementations • 10 Mar 2023 • Xiaopeng Jiang, Cristian Borcea
For improved model performance, these two types of complementary sparse models are aggregated into a dense model in each round, which is subsequently pruned in an iterative process.
no code implementations • 17 Nov 2021 • Xiaopeng Jiang, Han Hu, Vijaya Datta Mayyuri, An Chen, Devu M. Shila, Adriaan Larmuseau, Ruoming Jin, Cristian Borcea, NhatHai Phan
This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps.
no code implementations • 13 Jun 2021 • Xiaopeng Jiang, Shuai Zhao, Guy Jacobson, Rittwik Jana, Wen-Ling Hsu, Manoop Talasila, Syed Anwar Aftab, Yi Chen, Cristian Borcea
The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system.
no code implementations • 16 Oct 2018 • Shuai Zhao, Manoop Talasila, Guy Jacobson, Cristian Borcea, Syed Anwar Aftab, John F Murray
Applying Machine Learning (ML) to business applications for automation usually faces difficulties when integrating diverse ML dependencies and services, mainly because of the lack of a common ML framework.