  Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart
city such as the delivery of goods, mapping real-time road traffic and
monitoring pollution. The ability of drones to perform these functions often
requires the support of machine learning technology. However, traditional
machine learning models for drones encounter data privacy problems,
communication costs and energy limitations. Federated Learning, an emerging
distributed machine learning approach, is an excellent solution to address
these issues. Federated learning (FL) allows drones to train local models
without transmitting raw data. However, existing FL requires a central server
to aggregate the trained model parameters of the UAV. A failure of the central
server can significantly impact the overall training. In this paper, we propose
two aggregation methods: Commutative FL and Alternate FL, based on the existing
architecture of decentralised Federated Learning for UAV Networks (DFL-UN) by
adding a unique aggregation method of decentralised FL. Those two methods can
effectively control energy consumption and communication cost by controlling
the number of local training epochs, local communication, and global
communication. The simulation results of the proposed training methods are also
presented to verify the feasibility and efficiency of the architecture compared
with two benchmark methods (e.g. standard machine learning training and
standard single aggregation server training). The simulation results show that
the proposed methods outperform the benchmark methods in terms of operational
stability, energy consumption and communication cost.
