  In this paper we investigate the limit performance of Floating Gossip, a new,
fully distributed Gossip Learning scheme which relies on Floating Content to
implement location-based probabilistic evolution of machine learning models in
an infrastructure-less manner. We consider dynamic scenarios where continuous
learning is necessary, and we adopt a mean field approach to investigate the
limit performance of Floating Gossip in terms of amount of data that users can
incorporate into their models, as a function of the main system parameters.
Different from existing approaches in which either communication or computing
aspects of Gossip Learning are analyzed and optimized, our approach accounts
for the compound impact of both aspects. We validate our results through
detailed simulations, proving good accuracy. Our model shows that Floating
Gossip can be very effective in implementing continuous training and update of
machine learning models in a cooperative manner, based on opportunistic
exchanges among moving users.
