Reducing Non-IID Effects in Federated Autonomous Driving with Contrastive Divergence Loss
Tuong Do,Binh X. Nguyen,Quang D. Tran,Hien Nguyen,Erman Tjiputra,Te-Chuan Chiu,Anh Nguyen,Tuong Do,Binh X. Nguyen,Quang D. Tran,Hien Nguyen,Erman Tjiputra,Te-Chuan Chiu,Anh Nguyen
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 autonomous vehicles usually suffer from the non-independent-and-identically-distributed (non-IID) problem, which may cause negative effects on the convergence of the learning process. In this paper, we propose a new contrastive d...
