no code implementations • SemEval (NAACL) 2022 • Fadi Hassan, Wondimagegnhue Tufa, Guillem Collell, Piek Vossen, Lisa Beinborn, Adrian Flanagan, Kuan Eeik Tan
This paper presents our system used to participate in task 11 (MultiCONER) of the SemEval 2022 competition.
no code implementations • 27 Jul 2021 • Farwa K. Khan, Adrian Flanagan, Kuan E. Tan, Zareen Alamgir, Muhammad Ammad-Ud-Din
We introduce the payload optimization method for federated recommender systems (FRS).
no code implementations • 12 Dec 2020 • Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez, David Sánchez, Adrian Flanagan, Kuan Eeik Tan
In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Mika Juuti, Tommi Gröndahl, Adrian Flanagan, N. Asokan
Detection of some types of toxic language is hampered by extreme scarcity of labeled training data.
no code implementations • 8 Apr 2020 • Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, Muhammad Ammad-Ud-Din
We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources.
1 code implementation • 29 Jan 2019 • Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, Adrian Flanagan
In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates.