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.).

翻译：联邦学习（FL）作为一种分布式机器学习技术，近年来因其对用户数据隐私的重视而广受欢迎。然而，FL的分布式计算可能导致通信受限和学习过程冗长，因此需要优化客户端-服务器通信成本。客户选择比例和本地训练轮次数量是两个对FL性能有显著影响的超参数。由于不同应用场景的训练偏好各异，FL从业者手动选择此类超参数颇具挑战。本文提出FedAVO，一种利用非洲秃鹫优化器（AVO）选择最佳超参数以增强通信效率的新型FL算法。研究表明，通过采用AVO调整FL超参数，可显著降低FL运行过程中的通信成本。在基准数据集上对FedAVO进行广泛评估后，我们发现该算法在模型准确率和通信轮次方面均实现了显著提升，尤其在非独立同分布（Non-IID）数据集的现实场景中表现突出。通过全面评估FedAVO算法，我们识别出最适合基准数据集的最优超参数，最终使全局模型准确率较现有最优FL算法（如FedAvg、FedProx、FedPSO等）提升6%。