Search Results for author:
Found 3 papers, 3 papers with code
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy.
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC.
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
