  Federated learning (FL) enables devices in mobile edge computing (MEC) to
collaboratively train a shared model without uploading the local data. Gradient
compression may be applied to FL to alleviate the communication overheads but
current FL with gradient compression still faces great challenges. To deploy
green MEC, we propose FedGreen, which enhances the original FL with
fine-grained gradient compression to efficiently control the total energy
consumption of the devices. Specifically, we introduce the relevant operations
including device-side gradient reduction and server-side element-wise
aggregation to facilitate the gradient compression in FL. According to a public
dataset, we investigate the contributions of the compressed local gradients
with respect to different compression ratios. After that, we formulate and
tackle a learning accuracy-energy efficiency tradeoff problem where the optimal
compression ratio and computing frequency are derived for each device.
Experiments results demonstrate that given the 80% test accuracy requirement,
compared with the baseline schemes, FedGreen reduces at least 32% of the total
energy consumption of the devices.
