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.).

本研究介绍了一种新的联邦学习算法 FedAVO，它使用非洲秃鹫优化器 AVO 来选择最佳超参数以提高通信效果，并在基准数据集上进行了广泛的评估，证明了 FedAVO 在模型准确性和通信轮数方面的显着改进。我们发现采用 AVO 进行FL超参数调整可以显著降低通信成本，最终与其他国内外领先的算法相比增加全局模型准确度高达6%。

FedAVO: 利用非洲秃鹫优化器来提高联邦学习的通信效率