Federated Self-Supervised Learning of Monocular Depth Estimators for
  Autonomous Vehicles
Elton F. de S. Soares Carlos Alberto V. Campos
Image-based depth estimation has gained significant attention in recent
research on computer vision for autonomous vehicles in intelligent
transportation systems. This focus stems from its cost-effectiveness and wide
range of potential applications. Unlike binocular depth estimation methods that
require two fixed cameras, monocular depth estimation methods only rely on a
single camera, making them highly versatile. While state-of-the-art approaches
for this task leverage self-supervised learning of deep neural networks in
conjunction with tasks like pose estimation and semantic segmentation, none of
them have explored the combination of federated learning and self-supervision
to train models using unlabeled and private data captured by autonomous
vehicles. The utilization of federated learning offers notable benefits,
including enhanced privacy protection, reduced network consumption, and
improved resilience to connectivity issues. To address this gap, we propose
FedSCDepth, a novel method that combines federated learning and deep
self-supervision to enable the learning of monocular depth estimators with
comparable effectiveness and superior efficiency compared to the current
state-of-the-art methods. Our evaluation experiments conducted on Eigen's Split
of the KITTI dataset demonstrate that our proposed method achieves near
state-of-the-art performance, with a test loss below 0.13 and requiring, on
average, only 1.5k training steps and up to 0.415 GB of weight data transfer
per autonomous vehicle on each round.