Distributed client selection with multi-objective in federated learning
  assisted Internet of Vehicles
Narisu Cha Long Chang
Federated learning is an emerging distributed machine learning framework in
the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to
train the model to share their knowledge. Maintaining an active state means the
participants must update their state to the FL server in a fixed interval and
participate to next round. However, the cost by maintaining an active state is
very large when there are a huge number of participating vehicles. In this
paper, we proposed a distributed client selection scheme to reduce the cost of
maintaining the active state for all participants. The clients with the highest
evaluation are elected among the neighbours. In the evaluator, four variables
are considered including sample quantity, throughput available, computational
capability and the quality of the local dataset. We adopted fuzzy logic as the
evaluator since the closed-form solution over four variables does not exist.
Extensive simulation results show our proposal approximates the centralized
client selection in terms of accuracy and can significantly reduce the
communication overhead.