Risk-Aware Reinforcement Learning Based Federated Learning Framework for Io V

Federated learning helps protect data privacy for Internet of vehicles (Io V) by selecting a number of participated nodes but suffers from performance degradation such as low model training accuracy in the highly dynamic and large-scale Io V systems under selfish attacks. In this paper, we propose a risk-aware reinforcement learning based federated learning framework against selfish attacks for Io V,which jointly optimizes the training policy (i.e., the selection of participated vehicles and the corresponding local training data size) based on the state including the global model training accuracy, local model quality, training latency, data rate, and participation rate. By designing a punishment function to evaluate the immediate risk of each choosing training policy, this scheme avoids risky policies that result in extremely low training accuracy and high training latency to satisfy the requirements of local tasks such as the quality of service requirements. An evaluated neural network involved fully connected layers is designed to fast extract the global and local training features and thus accelerate the convergence speed. Experimental results based on both the MNIST and CIFAR-10 datasets verify that our scheme outperforms the benchmarks with higher training accuracy and less training latency.