Fedgcs: A generative framework for efficient client selection in federated learning via gradient-based optimization

Z Ning, C Tian, M **ao, W Fan, P Wang, L Li… - arxiv preprint arxiv …, 2024 - arxiv.org
Federated Learning faces significant challenges in statistical and system heterogeneity,
along with high energy consumption, necessitating efficient client selection strategies …

Ranking-based client imitation selection for efficient federated learning

C Tian, Z Shi, L Li, C Xu - Forty-first International Conference on …, 2024 - openreview.net
Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while ensuring data privacy. The selection of participating devices in each training round …

Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting

C Tian, L Li, K Tam, Y Wu, CZ Xu - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. Ever-increasing model complexity coupled with limited …

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning

C Tian, Z Shi, X Qin, L Li, C Xu - arxiv preprint arxiv:2405.04122, 2024 - arxiv.org
Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while ensuring data privacy. The selection of participating devices in each training round …

Reinforcement Learning-based Dual-Identity Double Auction in Personalized Federated Learning

J Li, Z Chen, T Zang, T Liu, J Wu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated learning participants have two identities: model trainers and model users. As
model users, participants care most about the performance of the final model on their own …