68 citations · 87 across the 20 of their papers we have counts for
6 papers · 1 filter
SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version
Tuan-Binh Tran, Dat Nguyen Cong, Duc-Trong Le +2
Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not…
Empowering Contrastive Federated Sequential Recommendation with LLMs
Thi Minh Chau Nguyen, Minh Hieu Nguyen, Duc Anh Nguyen +3
Federated sequential recommendation (FedSeqRec) aims to perform next-item prediction while keeping user data decentralised, yet model quality is frequently constrained by fragmente…
Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh +2
Multimodal federated learning in real-world settings often encounters incomplete and heterogeneous data across clients. This results in misaligned local feature representations tha…
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization
Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham +3
Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared gl…
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen +4
Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data. The increasing demand to address i…
A Survey of Machine Unlearning
Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren +4
Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (M…