1 code implementation • 22 Jan 2024 • Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
However, applying ICL in real cases does not scale with the number of samples, and lacks robustness to different prompt templates and demonstration permutations.
no code implementations • ICLR 2023 • Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
no code implementations • 16 Feb 2022 • Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
no code implementations • 15 Dec 2021 • Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
no code implementations • 21 Feb 2020 • Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, Amar Das
In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning.
no code implementations • 7 Oct 2019 • Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, Amar Das
We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.
1 code implementation • 14 Apr 2018 • Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, Kevin Chan
Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches.