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

Machine Learning;Distributed, Parallel, and Cluster Computing