Single channel speech denoising by DDPG reinforcement learning agent by Sania Gul (18272227)

Intelligent Bilateral Client Selection in Federated Learning Using Game Theory by Wehbi, Osama

“…Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. …”
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FoGMatch by Arisdakessian, Sarhad

“…To address this problem, we propose in this paper a multi-criteria intelligent IoT scheduling approach in fog computing environments using matching game theory. Our solution consists of (1) two optimization problems, one for the IoT devices and one for the fog nodes, (2) preference functions for both the IoT and fog layers to help them rank each other on the basis of several criteria such latency and resource utilization, and (3) centralized and distributed intelligent scheduling algorithms that consider the preferences of both the fog and IoT layers to improve the performance of the overall IoT ecosystem. …”
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