A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …
participants to collaboratively train a centralized model with privacy preservation by …
EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
The rise of mobile devices with abundant sensory data and local computing capabilities has
driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) …
driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) …
Empowering neural collaborative filtering with contextual features for multimedia recommendation
A rapid growth in multimedia on various application platforms has made essential the
provision of additional assistive technologies to handle information overload issues …
provision of additional assistive technologies to handle information overload issues …
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning
Z Qi, L Meng, W He, R Zhang, Y Wang, X Qi… - arXiv preprint arXiv …, 2024 - arxiv.org
Federated learning benefits from cross-training strategies, which enables models to train on
data from distinct sources to improve the generalization capability. However, the data …
data from distinct sources to improve the generalization capability. However, the data …
Feed: Towards Personalization-Effective Federated Learning
Federated learning (FL) has become an emerging paradigm via cooperative training models
among distributed clients without leaking data privacy. The performance degradation of F1 …
among distributed clients without leaking data privacy. The performance degradation of F1 …
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update
Z Niu, H Dong, AK Qin - arXiv preprint arXiv:2403.11464, 2024 - arxiv.org
Personalized Federated Learning (PFL) is widely employed in IoT applications to handle
high-volume, non-iid client data while ensuring data privacy. However, heterogeneous edge …
high-volume, non-iid client data while ensuring data privacy. However, heterogeneous edge …