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Federated learning is an emerging distributed machine learning framework inthe Internet of Vehicles (IoV). In IoV, millions of vehicles are willing totrain the model to share their knowledge. Maintaining an active state means theparticipants must update their state to the FL server in a fixed interval andparticipate to next round. However, the cost by maintaining an active state isvery large when there are a huge number of participating vehicles. In thispaper, we proposed a distributed client selection scheme to reduce the cost ofmaintaining the active state for all participants. The clients with the highestevaluation are elected among the neighbours. In the evaluator, four variablesare considered including sample quantity, throughput available, computationalcapability and the quality of the local dataset. We adopted fuzzy logic as theevaluator since the closed-form solution over four variables does not exist.Extensive simulation results show our proposal approximates the centralizedclient selection in terms of accuracy and can significantly reduce thecommunication overhead.

Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles