%Aigaion2 BibTeX export von HES SO Valais Publications
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@INPROCEEDINGS{IEEE WCNC,
     author = {Rizzo, Gianluca and Dinani, Mina Aghaei},
      month = mar,
      title = {Gossip Learning of Personalized Models for Vehicle Trajectory Prediction},
       year = {2021},
  publisher = {IEEE},
   abstract = {Gossip Learning (GL) is a peer-to-peer machine
learning protocol based on direct, opportunistic exchange of
models among nodes via wireless D2D communications, and on
collaborative model training, which has recently proven to scale
efficiently to large numbers of nodes, and to offer better privacy
guarantees than traditional centralized learning architectures.
Existing approaches to GL are however limited to scenarios
in which nodes are static, or in which the node connectivity
graph is fully connected, and they are fragile to node churn
as well as to any change in network configuration. To overcome
this limitation, we present a new decentralized architecture for
GL suitable for setups with dynamic nodes, which benefits from
node mobility instead of being hampered by it. In our approach,
nodes improve their personalized model instance by sharing
it with neighbors, and by weighting neighbors’ contributions
according to an estimate of their marginal utility. We apply our
GL algorithm to short-term vehicular trajectory estimation in
realistic urban scenarios. We propose a new strategy for the
estimation of the neighbors’ instances marginal utility, which
yields satisfactory trajectory estimation accuracy for nodes with
long enough sojourn times.}
}