FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning

As an alternative centralized systems, which may prevent data to be stored in a central repository due to its privacy and/or abundance, federated learning (FL) is nowadays a game changer addressing both privacy and cooperative learning. It succeeds in keeping training data on the devices, while sharing locally computed then globally aggregated models throughout several communication rounds. The selection of clients participating in FL process is currently at complete/quasi randomness. However, the heterogeneity of the client devices within Internet-of-Things environment and their limited communication and computation resources might fail to complete the training task, which may lead to many discarded learning rounds affecting the model accuracy. In this article, we propose FedMCCS, a multicriteria-based approach for client selection in FL. All of the CPU, memory, energy, and time are considered for the clients resources to predict whether they are able to perform the FL task. Particularly, in each round, the number of clients in FedMCCS is maximized to the utmost, while considering each client resources and its capability to successfully train and send the needed updates. The conducted experiments show that FedMCCS outperforms the other approaches by: 1) reducing the number of communication rounds to reach the intended accuracy; 2) maximizing the number of clients; 3) handling the least number of discarded rounds; and 4) optimizing the network traffic.

Automated summary

This article introduces FedMCCS, a multi-criteria client selection method for addressing the issue of limited client resources leading to decreased model accuracy in federated learning. FedMCCS outperforms other approaches in reducing communication rounds, maximizing the number of clients, handling the least number of discarded rounds, and optimizing network traffic.