104 citations · 147 across the 5 of their papers we have counts for
8 papers · 1 filter
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training
Haoyuan Li, Mathias Funk, Jindong Wang +1
Federated Active Learning (FAL) has emerged as a promising framework to leverage large quantities of unlabeled data across distributed clients while preserving data privacy. Howeve…
Collaboratively Learning Federated Models from Noisy Decentralized Data
Haoyuan Li, Mathias Funk, Nezihe Merve Gürel +1
Federated learning (FL) has emerged as a prominent method for collaboratively training machine learning models using local data from edge devices, all while keeping data decentrali…
Consistency Training of Multi-exit Architectures for Sensor Data
Aaqib Saeed
Deep neural networks have become larger over the years with increasing demand of computational resources for inference; incurring exacerbate costs and leaving little room for deplo…
ProcessTransformer: Predictive Business Process Monitoring with Transformer Network
Zaharah A. Bukhsh, Aaqib Saeed, Remco M. Dijkman
Predictive business process monitoring focuses on predicting future characteristics of a running process using event logs. The foresight into process execution promises great poten…
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Aaqib Saeed, Flora D. Salim, Tanir Ozcelebi +1
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to p…
Multi-task Self-Supervised Learning for Human Activity Detection
Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien
Deep learning methods are successfully used in applications pertaining to ubiquitous computing, health, and well-being. Specifically, the area of human activity recognition (HAR) i…