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Federated Learning (FL), a distributed machine learning technique hasrecently experienced tremendous growth in popularity due to its emphasis onuser data privacy. However, the distributed computations of FL can result inconstrained communication and drawn-out learning processes, necessitating theclient-server communication cost optimization. The ratio of chosen clients andthe quantity of local training passes are two hyperparameters that have asignificant impact on FL performance. Due to different training preferencesacross various applications, it can be difficult for FL practitioners tomanually select such hyperparameters. In our research paper, we introduceFedAVO, a novel FL algorithm that enhances communication effectiveness byselecting the best hyperparameters leveraging the African Vulture Optimizer(AVO). Our research demonstrates that the communication costs associated withFL operations can be substantially reduced by adopting AVO for FLhyperparameter adjustment. Through extensive evaluations of FedAVO on benchmarkdatasets, we show that FedAVO achieves significant improvement in terms ofmodel accuracy and communication round, particularly with realistic cases ofNon-IID datasets. Our extensive evaluation of the FedAVO algorithm identifiesthe optimal hyperparameters that are appropriately fitted for the benchmarkdatasets, eventually increasing global model accuracy by 6% in comparison tothe state-of-the-art FL algorithms (such as FedAvg, FedProx, FedPSO, etc.).

FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer