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，近年来因强调用户数据隐私而受到极大关注。然而，FL的分布式计算可能导致通信受限和冗长的学习过程，因此需要优化客户端-服务器通信成本。所选客户端比例与本地训练轮次数量这两个超参数对FL性能具有重大影响。由于不同应用场景存在训练偏好差异，FL从业者难以手动选取此类超参数。本文提出FedAVO这一新型FL算法，通过利用非洲秃鹫优化器（AVO）选取最优超参数来提升通信效率。研究表明，采用AVO进行FL超参数调整可显著降低FL操作带来的通信成本。通过在基准数据集上的广泛评估，我们发现FedAVO在模型精度和通信轮次方面实现显著提升，尤其在处理非独立同分布数据集的真实场景中表现突出。对FedAVO算法的全面评估确定了最适合基准数据集的最优超参数，最终使全局模型精度相比现有最先进FL算法（如FedAvg、FedProx、FedPSO等）提升6%。