Gossip learning (GL), as a decentralized alternative to federated learning
(FL), is more suitable for resource-constrained wireless networks, such as
FANETs that are formed by unmanned aerial vehicles (UAVs). GL can significantly
enhance the efficiency and extend the battery life of UAV networks. Despite the
advantages, the performance of GL is strongly affected by data distribution,
communication speed, and network connectivity. However, how these factors
influence the GL convergence is still unclear. Existing work studied the
convergence of GL based on a virtual quantity for the sake of convenience,
which fail to reflect the real state of the network when some nodes are
inaccessible. In this paper, we formulate and investigate the impact of
inaccessible nodes to GL under a dynamic network topology. We first decompose
the weight divergence by whether the node is accessible or not. Then, we
investigate the GL convergence under the dynamic of node accessibility and
theoretically provide how the number of inaccessible nodes, data
non-i.i.d.-ness, and duration of inaccessibility affect the convergence.
Extensive experiments are carried out in practical settings to comprehensively
verify the correctness of our theoretical findings.

研究了数据分布、通信速度和网络连接性如何影响八无人机网络中八不可访问节点对闲聊学习收敛的影响，旨在提高效率并延长电池寿命。