Abstract
In this paper, the problem of drone-assisted collaborative learning is
considered. In this scenario, swarm of intelligent wireless devices train a
shared neural network (NN) model with the help of a drone. Using its sensors,
each device records samples from its environment to gather a local dataset for
training. The training data is severely heterogeneous as various devices have
different amount of data and sensor noise level. The intelligent devices
iteratively train the NN on their local datasets and exchange the model
parameters with the drone for aggregation. For this system, the convergence
rate of collaborative learning is derived while considering data heterogeneity,
sensor noise levels, and communication errors, then, the drone trajectory that
maximizes the final accuracy of the trained NN is obtained. The proposed
trajectory optimization approach is aware of both the devices data
characteristics (i.e., local dataset size and noise level) and their wireless
channel conditions, and significantly improves the convergence rate and final
accuracy in comparison with baselines that only consider data characteristics
or channel conditions. Compared to state-of-the-art baselines, the proposed
approach achieves an average 3.85% and 3.54% improvement in the final accuracy
of the trained NN on benchmark datasets for image recognition and semantic
segmentation tasks, respectively. Moreover, the proposed framework achieves a
significant speedup in training, leading to an average 24% and 87% saving in
the drone hovering time, communication overhead, and battery usage,
respectively for these tasks.