%Aigaion2 BibTeX export from HES SO Valais Publications
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@INPROCEEDINGS{ACM MobiHoc,
    author = {Rizzo, Gianluca and Dinani, Mina Aghaei and Maio, Antonio Di},
  keywords = {Distributed Learning, IoT},
     month = sep,
     title = {The Upsides of Turbulence: Baselining Gossip Learning in Dynamic Settings},
      year = {2023},
       url = {https://dl.acm.org/doi/abs/10.1145/3565287.3616530},
       doi = {10.1145/3565287.3616530},
  abstract = {In dynamic settings, fully distributed gossip-based learning schemes
have recently gained interest due to their better scalability, robustness,
and enhanced privacy protection compared to server-based
architectures. However, existing approaches to their performance
characterization either assume stable connectivity among nodes
or are ad-hoc for specific trace-based mobility patterns. Thus, in
dynamic settings, there is currently a poor understanding of the
conditions under which gossip-based learning schemes are feasible,
and of their main performance tradeoffs. In this work, we start
addressing this issue by performing a first baselining of Gossip
Learning (GL) on random Time-Varying Graphs (TVG), to get a
first-order characterization of their main performance patterns in
dynamic settings. The use of random TVG enables a fine-grained
and accurate characterization of GL effectiveness as a function
of the main system parameters while abstracting from scenariospecific
features of patterns of communication and mobility (e.g.,
induced by road grids or measured mobility traces). Our results suggest
that GL schemes are robust to node mobility and comparable
in accuracy and convergence speed to Federated Learning architectures,
over a wide range of operational conditions. We show that
the final model accuracy is robust against data dispersion across
nodes as well as against very low rates of exchanges across nodes.}
}