Search Results for author: Kuan Eeik Tan

Found 6 papers, 1 papers with code

Digital Forgetting in Large Language Models: A Survey of Unlearning Methods

no code implementations2 Apr 2024 Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares, David Sánchez, Josep Domingo-Ferrer, Guillem Collell, Kuan Eeik Tan

The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present.

Machine Unlearning

Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions

no code implementations12 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.

Federated Learning

Federated Multi-view Matrix Factorization for Personalized Recommendations

no code implementations8 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.

Federated Learning

Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification

no code implementations20 Jan 2020 Tinghuai Wang, Guangming Wang, Kuan Eeik Tan, Donghui Tan

Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces.

Classification General Classification +2

Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

1 code implementation29 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.

BIG-bench Machine Learning Collaborative Filtering +2

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