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@ARTICLE{,
    author = {Rizzo, Gianluca},
  keywords = {Distributed Learning, Gossip Learning},
     month = apr,
     title = {Floating Gossip: Serverless Distributed Learning in Dynamic Scenarios},
   journal = {IEEE Transactions on Mobile Computing},
      year = {2026},
       doi = {10.1109/TMC.2026.3682564},
  abstract = {This paper studies the performance of Floating
Gossip, a novel decentralized approach for Gossip Learning
at the network edge. Floating Gossip utilizes Floating Content
to facilitate location-based probabilistic evolution of Machine
Learning models, without external infrastructure support. We
investigate dynamic scenarios requiring continuous learning,
leveraging a mean field approach to analyze Floating Gossip’s
performance boundaries. Our focus is on the quantity of data that
users can integrate into their models, as a function of key system
parameters. Unlike previous studies that separately optimize
communication or computational aspects of Gossip Learning,
our methodology considers their combined effect. We validate
our analysis through comprehensive simulations, demonstrating
the high accuracy of our analytical model. Our methodology
reveals Floating Gossip’s effectiveness in training and updating
Machine Learning models collaboratively, leveraging opportunis-
tic exchanges between mobile users, while flexibly adapting to
different user characteristics and mobility patterns. This research
highlights Floating Gossip’s potential for continuous, cooperative
model training in dynamic, infrastructure-less environments,
offering insight into its performance patterns and its potential in
practical applications.}
}