Privacy-sensitive data is stored in autonomous vehicles, smart devices, or
sensor nodes that can move around with making opportunistic contact with each
other. Federation among such nodes was mainly discussed in the context of
federated learning with a centralized mechanism in many works. However, because
of multi-vendor issues, those nodes do not want to rely on a specific server
operated by a third party for this purpose. In this paper, we propose a
wireless ad hoc federated learning (WAFL) -- a fully distributed cooperative
machine learning organized by the nodes physically nearby. WAFL can develop
generalized models from Non-IID datasets stored in distributed nodes locally by
exchanging and aggregating them with each other over opportunistic node-to-node
contacts. In our benchmark-based evaluation with various opportunistic
networks, WAFL has achieved higher accuracy of 94.8-96.3% than the
self-training case of 84.7%. All our evaluation results show that WAFL can
train and converge the model parameters from highly-partitioned Non-IID
datasets over opportunistic networks without any centralized mechanisms.Comment: 14 pages, 8 figures, 2 table