FedFaSt: Selective Federated Learning using Fittest Parameters Aggregation and Slotted Clients Training

This paper proposes a novel selective federated learning (FL) algorithm, called fittest aggregation and slotted training (FedFaSt). It relies on a "free-for-all" client training process to score clients' efficiency while applying the "natural selection" principle to elect the fittest clients to be used in FL training and aggregation processes. While relying on a combined data quality and training performance metric for scoring clients, FedFaSt implements a slotted training model enabling teams of fittest clients to participate in the training and aggregation processes for a fixed number of successive rounds, called slots. Performance validation using X-ray datasets reveals that FedFaSt outperforms selective federated learning algorithms like FedAVG, FedRand, and FedPow in terms of accuracy, convergence to the global optimum, time complexity, and robustness against attacks.