  With the development and progress of science and technology, the Internet of
Things(IoT) has gradually entered people's lives, bringing great convenience to
our lives and improving people's work efficiency. Specifically, the IoT can
replace humans in jobs that they cannot perform. As a new type of IoT vehicle,
the current status and trend of research on Unmanned Aerial Vehicle(UAV) is
gratifying, and the development prospect is very promising. However, privacy
and communication are still very serious issues in drone applications. This is
because most drones still use centralized cloud-based data processing, which
may lead to leakage of data collected by drones. At the same time, the large
amount of data collected by drones may incur greater communication overhead
when transferred to the cloud. Federated learning as a means of privacy
protection can effectively solve the above two problems. However, federated
learning when applied to UAV networks also needs to consider the heterogeneity
of data, which is caused by regional differences in UAV regulation. In
response, this paper proposes a new algorithm FedBA to optimize the global
model and solves the data heterogeneity problem. In addition, we apply the
algorithm to some real datasets, and the experimental results show that the
algorithm outperforms other algorithms and improves the accuracy of the local
model for UAVs.
