Reducing Non-IID Effects in Federated Autonomous Driving with Contrastive Divergence Loss
Federated learning has been widely applied in autonomous driving since it enables training
a learning model among vehicles without sharing users' data. However, data from …
a learning model among vehicles without sharing users' data. However, data from …
Performance Profiling of Federated Learning Across Heterogeneous Mobile Devices
JA Esquivel, B Aldous… - 2024 IEEE 24th …, 2024 - ieeexplore.ieee.org
As mobile devices increasingly become more widely used in data analytics and machine
learning, Federated Learning (FL) offers a promising decentralised approach that maintains …
learning, Federated Learning (FL) offers a promising decentralised approach that maintains …
[PDF][PDF] Addressing Non-IID Problem in Federated Autonomous Driving with Contrastive Divergence Loss
Federated learning has been widely applied in autonomous driving since it enables training
a learning model among vehicles without sharing users' data. However, data from …
a learning model among vehicles without sharing users' data. However, data from …