  Edge computing has revolutionized the world of mobile and wireless networks
world thanks to its flexible, secure, and performing characteristics. Lately,
we have witnessed the increasing use of it to make more performing the
deployment of machine learning (ML) techniques such as federated learning (FL).
FL was debuted to improve communication efficiency compared to conventional
distributed machine learning (ML). The original FL assumes a central
aggregation server to aggregate locally optimized parameters and might bring
reliability and latency issues. In this paper, we conduct an in-depth study of
strategies to replace this central server by a flying master that is
dynamically selected based on the current participants and/or available
resources at every FL round of optimization. Specifically, we compare different
metrics to select this flying master and assess consensus algorithms to perform
the selection. Our results demonstrate a significant reduction of runtime using
our flying master FL framework compared to the original FL from measurements
results conducted in our EdgeAI testbed and over real 5G networks using an
operational edge testbed.
