no code implementations • 8 Nov 2023 • Yashothara Shanmugarasa, M. A. P. Chamikara, Hye-Young Paik, Salil S. Kanhere, Liming Zhu
In this paper, we propose a novel LDP approach (named LDP-SmartEnergy) that utilizes randomized response techniques with sliding windows to facilitate the sharing of appliance-level energy consumption data over time while not revealing individual users' appliance usage patterns.
no code implementations • 11 Aug 2023 • Yue Liu, Qinghua Lu, Liming Zhu, Hye-Young Paik
Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks.
no code implementations • 18 Aug 2022 • Mariya Shmalko, Alsharif Abuadbba, Raj Gaire, Tingmin Wu, Hye-Young Paik, Surya Nepal
The Profiler does not require large data sets to train on to be effective and its analysis of varied email features reduces the impact of concept drift.
no code implementations • 28 Apr 2022 • Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu
Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning.
1 code implementation • 2 Sep 2021 • Xuesong Wang, Lina Yao, Xianzhi Wang, Hye-Young Paik, Sen Wang
Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).
no code implementations • 16 Aug 2021 • Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang, Xiwei Xu, Hye-Young Paik, Liming Zhu
To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.
no code implementations • 22 Jun 2021 • Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu
The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation.
no code implementations • 13 Mar 2021 • Nicholas Malecki, Hye-Young Paik, Aleksandar Ignjatovic, Alan Blair, Elisa Bertino
Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of their training data and their hardware.
no code implementations • 7 Jan 2021 • Sin Kit Lo, Qinghua Lu, Liming Zhu, Hye-Young Paik, Xiwei Xu, Chen Wang
Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems.
no code implementations • 22 Jul 2020 • Sin Kit Lo, Qinghua Lu, Chen Wang, Hye-Young Paik, Liming Zhu
Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates.
1 code implementation • 15 Jan 2019 • Rahul Anand, Hye-Young Paik, Cheng Wang
Tabular data extraction from reports and other published data in PDF format is of interest for various data consolidation purposes such as analysing and aggregating financial reports of a company.