  Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support
extensive applications in next-generation wireless networks in both civil and
military fields. Empowering UAVs networks intelligence by artificial
intelligence (AI) especially machine learning (ML) techniques is inevitable and
appealing to enable the aforementioned applications. To solve the problems of
traditional cloud-centric ML for UAV networks such as privacy concern,
unacceptable latency, and resource burden, a distributed ML technique, i.e.,
federated learning (FL), has been recently proposed to enable multiple UAVs to
collaboratively train ML model without letting out raw data. However, almost
all existing FL paradigms are server-based, i.e., a central entity is in charge
of ML model aggregation and fusion over the whole network, which could result
in the issue of a single point of failure and are inappropriate to UAV networks
with both unreliable nodes and links. To address the above issue, in this
article, we propose a novel architecture called SELF-UN
(\underline{SE}rver\underline{L}ess \underline{F}L for \underline{U}AV
\underline{N}etworks), which enables FL within UAV networks without a central
entity. We also conduct a preliminary simulation study to validate the
feasibility and effectiveness of the SELF-UN architecture. Finally, we discuss
the main challenges and potential research directions in the SELF-UN.
