Evolutionary Model Owner Selection for Federated Learning with Heterogeneous Privacy Budgets

Leveraging on the wealth of data and advancements in Artificial Intelligence, smart cities have demonstrated their great potential in providing solutions to challenges that the urban population faces today. However, as the urban population becomes more privacy sensitive and with the introduction of stringent privacy regulations, the differential-private FL (DPFL) is a promising technology that can enable privacy-preserving collaborative model training. In this paper, we consider an FL network of model owners and data owners with heterogeneous privacy budgets and preferences respectively. In exchange for their participation in the training, the model owner offers a reward pool that is shared among the data owners that take part in the FL training. In turn, the FL worker with heterogeneous privacy preferences may select the model owner to contribute its parameters to. To model the dynamic and strategic behaviour of the workers in the process of model owner selection, we propose an evolutionary game approach. Then, we conduct simulations to validate the evolutionary equilibrium, as well as provide the sensitivity analyses of the model.