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@ARTICLE{,
    author = {Rizzo, Gianluca and Dinani, Mina Aghaei and Marsan, Marco G Ajmone},
  keywords = {Distributed Learning, Gossip Learning},
     month = sep,
     title = {A Gossip Learning Approach to Urban Trajectory Nowcasting for Anticipatory RAN Management},
   journal = {IEEE Transactions on Mobile Computing},
      year = {2023},
       url = {https://ieeexplore.ieee.org/abstract/document/10266757},
       doi = {10.1109/TMC.2023.3320551},
  abstract = {In future radio access networks, machine learning
(ML) based strategies for short-term forecasting of vehicular
trajectories will be key for anticipatory resource allocation and
management at the mobile edge. However, training ML models
in a centralized fashion, over data collected from a massive
heterogeneous and dynamic set of devices, poses significant
scalability, reliability, and efficiency challenges, which are still
open to date.
In this paper, we look at the specific issue of scalable and resource-
efficient training of ML models in a vehicular environment. To
address such a challenge, we propose a new Gossip Learning
scheme, i.e., a fully distributed, collaborative training approach
based on direct, opportunistic model exchanges via wireless
device-to-device (D2D) communications with no centralized sup-
port. Our approach is based on constantly improving each node’s
own model instance through knowledge transfer among nodes,
and on different strategies for estimating the potential contri-
bution of neighboring nodes to the training process at a node.
Extensive numerical assessments on a variety of measurement-
based dynamic urban scenarios suggest that our schemes are able
to converge rapidly and provide sufficiently accurate forecasts of
vehicle position for time horizons which are typical of future
5G/6G dynamic resource allocation algorithms.}
}