4 citations · 8 across the 5 of their papers we have counts for
3 papers · 1 filter
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients
Minh Duong Nguyen, Khanh Le, Khoi Do +4
In personalized Federated Learning (pFL), high data heterogeneity can cause significant gradient divergence across devices, adversely affecting the learning process. This divergenc…
Orthogonal Gated Recurrent Unit with Neumann-Cayley Transformation
Edison Mucllari, Vasily Zadorozhnyy, Cole Pospisil +2
In recent years, using orthogonal matrices has been shown to be a promising approach in improving Recurrent Neural Networks (RNNs) with training, stability, and convergence, partic…
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning
Nang Hung Nguyen, Phi Le Nguyen, Duc Long Nguyen +4
The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning…