  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.
