_:nd0e89ac706b141b1ad464605407a657cxb20
<http://schema.org/description>
"Federated Learning (FL) is a novel distributed privacy-preserving learning\nparadigm, which enables the collaboration among several participants (e.g.,\nInternet of Things devices) for the training of machine learning models.\nHowever, selecting the participants that would contribute to this collaborative\ntraining is highly challenging. Adopting a random selection strategy would\nentail substantial problems due to the heterogeneity in terms of data quality,\nand computational and communication resources across the participants. Although\nseveral approaches have been proposed in the literature to overcome the problem\nof random selection, most of these approaches follow a unilateral selection\nstrategy. In fact, they base their selection strategy on only the federated\nserver's side, while overlooking the interests of the client devices in the\nprocess. To overcome this problem, we present in this paper FedMint, an\nintelligent client selection approach for federated learning on IoT devices\nusing game theory and bootstrapping mechanism. Our solution involves the design\nof: (1) preference functions for the client IoT devices and federated servers\nto allow them to rank each other according to several factors such as accuracy\nand price, (2) intelligent matching algorithms that take into account the\npreferences of both parties in their design, and (3) bootstrapping technique\nthat capitalizes on the collaboration of multiple federated servers in order to\nassign initial accuracy value for the newly connected IoT devices. Based on our\nsimulation findings, our strategy surpasses the VanillaFL selection approach in\nterms of maximizing both the revenues of the client devices and accuracy of the\nglobal federated learning model."
<https://api.deepai.org/profile/osama-wehbi>