Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyper-parameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyper-parameters and genetically modifies the parameters cluster-wise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyper-parameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data.
翻译：联邦学习(FL)是将客户-服务器结构、边缘计算和实时智能结合起来的深层次学习的分布模式,FL具有使机器学习(ML)革命化的能力,但由于技术限制、通信管理费、非IID(独立和同样分布)数据和隐私问题,FL缺乏实施的实际性。对多种非IID数据进行ML模型培训,会大大降低趋同率和性能。现有的传统和集群FL算法存在两个主要局限性,包括客户培训效率低下和静态超光谱利用。为了克服这些局限性,我们建议采用新型混合算法,即基因集群FL(Genetic CFL)(Generictic CFL)(Group ) (ML) (ML) (ML) ) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (Meless 能力,但由于技术限制、通信限制、通信管理、通信管理、通信管理、通信管理、通信管理、通信管理、通信管理、非IID(独立和不切合而缺乏分配) (独立和不可行性数据) (非IID) 数据,但缺乏私隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐隐性能而缺乏而缺乏而缺乏而缺乏实际性而缺乏实际性。然后我们。我们引入一种算算法。我们引入一种算法,而引入一种算法,而采用一种计算法,而使个人集性地改变性地能边装置(ML) (M) (M) (ML(ML) (I) (I) (I(I) (I) (I) (I) (I) (I) (I) (I) (I) (ID) (ID) (I) (ID) (不相能边线) (不相能) 和基因基) (I) (ID) (I) (ID) (I) (I) (I) (IL) (
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