no code implementations • 1 Mar 2024 • Jinhyun So, HyukJoon Kwon
Existing auto-encoder (AE)-based channel state information (CSI) frameworks have focused on a specific configuration of user equipment (UE) and base station (BS), and thus the input and output sizes of the AE are fixed.
no code implementations • 2 Feb 2022 • Jinhyun So, Kevin Hsieh, Behnaz Arzani, Shadi Noghabi, Salman Avestimehr, Ranveer Chandra
To address these challenges, we leverage Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites.
no code implementations • 5 Oct 2021 • Jinhyun So, Ramy E. Ali, Başak Güler, A. Salman Avestimehr
A buffered asynchronous training protocol known as FedBuff has been proposed recently which bridges the gap between synchronous and asynchronous training to mitigate stragglers and to also ensure privacy simultaneously.
no code implementations • 29 Sep 2021 • Jinhyun So, Chaoyang He, Chien-Sheng Yang, Songze Li, Qian Yu, Ramy E. Ali, Basak Guler, Salman Avestimehr
We also demonstrate that, unlike existing schemes, LightSecAgg can be applied to secure aggregation in the asynchronous FL setting.
no code implementations • 7 Jun 2021 • Jinhyun So, Ramy E. Ali, Basak Guler, Jiantao Jiao, Salman Avestimehr
In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users.
no code implementations • 11 Nov 2020 • Ramy E. Ali, Jinhyun So, A. Salman Avestimehr
In this work, we empirically show that the square function is not the best degree-2 polynomial that can replace the ReLU function even when restricting the polynomials to have integer coefficients.
no code implementations • NeurIPS 2020 • Jinhyun So, Basak Guler, A. Salman Avestimehr
We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from the other parties.
5 code implementations • 27 Jul 2020 • Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr
Federated learning (FL) is a rapidly growing research field in machine learning.
no code implementations • 21 Jul 2020 • Jinhyun So, Basak Guler, A. Salman Avestimehr
This presents a major challenge for the resilience of the model against adversarial (Byzantine) users, who can manipulate the global model by modifying their local models or datasets.
no code implementations • 11 Feb 2020 • Jinhyun So, Basak Guler, A. Salman Avestimehr
A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users.
no code implementations • 2 Feb 2019 • Jinhyun So, Basak Guler, A. Salman Avestimehr
How to train a machine learning model while keeping the data private and secure?