FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training
The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …
train models without exposing their raw data. In most cases, the data across devices are non …
FAST: Enhancing Federated Learning through Adaptive Data Sampling and Local Training
Z Wang, H Xu, Y Xu, Z Jiang, J Liu… - IEEE Transactions on …, 2023 - computer.org
The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …
train models without exposing their raw data. In most cases, the data across devices are non …
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training
Z Wang, H Xu, Y Xu, Z Jiang, J Liu, S Chen - IEEE Transactions on …, 2024 - dl.acm.org
The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …
train models without exposing their raw data. In most cases, the data across devices are non …
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training
Z Wang, H Xu, Y Xu, Z Jiang, J Liu… - IEEE Transactions on …, 2024 - computer.org
The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …
train models without exposing their raw data. In most cases, the data across devices are non …