1 citations · 2 across the 3 of their papers we have counts for
4 papers
Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering
Haemin Park, Diego Klabjan, Martin W. Braun +2
Federated deep clustering seeks to learn clustering-friendly representations from decentralized unlabeled data while preserving client privacy. However, Deep Embedded Clustering (D…
Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
Haemin Park, Diego Klabjan, Martin W. Braun +2
Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot be addressed by standard ce…
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling
Jaeyeon Jang, Diego Klabjan, Han Liu +5
Real-time dynamic scheduling is a crucial but notoriously challenging task in modern manufacturing processes due to its high decision complexity. Recently, reinforcement learning (…
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints
Jaeyeon Jang, Diego Klabjan, Han Liu +5
This paper proposes a novel multi-agent reinforcement learning (MARL) method to learn multiple coordinated agents under directed acyclic graph (DAG) constraints. Unlike existing MA…