RiCAM: An Efficient Multicriteria Client Selection Mechanism for Federated Learning

The usual Machine Learning (ML) uses raw clients in a centralized mechanism and generalized training, which demands a lot of time and processing to send the network this sensitive data and to process only on Edge. Federated Learning (FL) improves the ML by using local training in each client device, selecting some specific clients to train, and sending only their local parameters for an edge. In this way, a client selection mechanism is essential to select clients to ensure data diversity and high-quality data, which enables global model to generalize well across different data distributions. In addition, while high-quality data contributes to the accuracy and reliability of the learned model. However, it is important to asses the device importance based on multiple criteria, aiding in identifying and prioritizing the most valuable and promising clients for participation in the training process. In this paper, we introduce a Robust multi-criteria Client Selection Mechanism called RICAm, which address the challenges of non-IID data FL environments. RiCAm uses client selection to search for the best-fit clients to train, calculated using the multicriteria method MACBETH. In our evaluation, RiCAm reached only 16 rounds, 80% of which is around 10% more than the standard random selection approach random selection approach.