Deep Federated Learning for Autonomous Driving

Related tags

Deep Learning deep-learning autonomous autonomous-driving federated-learning deep-federated-learning
Overview

FADNet: Deep Federated Learning for Autonomous Driving

Abstract

Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Therefore, these methods do not take into account the user's privacy. In this paper, we present a new approach to learn autonomous driving policy while respecting privacy concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to train deep architectures in a fully decentralized manner and remove the need for central orchestration. We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods. Intensively experimental results on three datasets show that our approach with FADNet and DFL achieves superior accuracy compared with other recent methods. Furthermore, our approach can maintain privacy by not collecting user data to a central server.

Code & Data

Coming soon

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Comments
  • Some confusions about network structure

    Some confusions about network structure

    Hi, AIOZ AI! Thanks for your great work.

    I'm a little confused about some of the snippets in the code.

    1. According to Fig 3 in the paper, Feature 1, Feature 2 and Feature 3 are not the output of ResBlock1, ResBlock2 and ResBlock3 respectively. However, the code is implemented like this.

      def forward(self, inputs):

 x1 = self.norm(inputs)
 x1 = self.conv1(x1)
 x1 = self.max_pool1(x1)

 x2 = self.res_block1(x1)
 x1 = self.conv2(x1)
 x3 = torch.add(x1, x2)

 x3_1 = x3.view(inputs.shape[0], -1)

 x4 = self.res_block2(x3)
 x3 = self.conv3(x3)
 x4 = torch.add(x3, x4)

 x4_1 = x4.view(inputs.shape[0], -1)

 x5 = self.res_block3(x4)
 x4 = self.conv4(x4)
 x5 = torch.add(x4, x5)

 x6 = x5.view(inputs.shape[0], -1)
 x6 = self.relu(x6)
 x6 = self.dropout(x6)

 return 0.7*self.fc(x6) + 0.1*x3_1.mean() + 0.1*x4_1.mean() + 0.1*x6.mean()

    2. In this line of code("0.7self.fc(x6) + 0.1x3_1.mean() + 0.1x4_1.mean() + 0.1x6.mean()"), what is the basis for choosing the weights ?

    opened by JackFroster 3
Owner
AIOZ AI
Research and Develop (R&D) proprietary AI & Deep technologies for intelligent video analytics, mobile robotics, smart city, and beyond.
AIOZ AI
GitHub
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