  Federated learning (FL) has been increasingly considered to preserve data
training privacy from eavesdropping attacks in mobile edge computing-based
Internet of Thing (EdgeIoT). On the one hand, the learning accuracy of FL can
be improved by selecting the IoT devices with large datasets for training,
which gives rise to a higher energy consumption. On the other hand, the energy
consumption can be reduced by selecting the IoT devices with small datasets for
FL, resulting in a falling learning accuracy. In this paper, we formulate a new
resource allocation problem for privacy-persevering EdgeIoT to balance the
learning accuracy of FL and the energy consumption of the IoT device. We
propose a new federated learning-enabled twin-delayed deep deterministic policy
gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance
in a continuous domain. Furthermore, long short term memory (LSTM) is leveraged
in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained
to select the IoT devices and allocate the transmit power. Numerical results
demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100
iterations) while the FL accuracy-to-energy consumption ratio is improved by
51.8% compared to existing state-of-the-art benchmark.
