Resilient federated learning under data and system heterogeneity via genetic algorithm-based group client selection

The process of selecting suitable clients to participate in Federated Learning (FL) remains a critical challenge due to non-IID heterogeneity in data distributions and heterogeneity in computational resources among participating clients. Existing client selection approaches typically assess clients individually using attributes such as model accuracy. Such individual-based client assessment ignores group effects, which are paramount for global model performance. In this paper, we propose a Genetic-Algorithm (GA) based client selection mechanism that is applicable to both horizontal FL (HFL) and vertical FL (VFL). Candidate client groups are evaluated by a multi-criteria fitness function that jointly models group data size, feature coverage, label coverage, class balance, feature-distribution divergence, feature importance, computational power, reputation, accuracy, and outlier proportion. The GA performs an efficient search over the combinatorial space of client subsets. The proposed approach is evaluated using image classification, diabetes prediction, and rain prediction tasks. The proposed approach improves global model accuracy and accelerates convergence. On the MNIST dataset, it peaks at 98.37%, outperforming DSCS (97.27%) and FedMCCS (97.07%). On the diabetes dataset, it achieves 88.37%, while DSCS and FedMCCS achieve 87.15% and 86.64%, respectively. On the rain prediction task, it attains a peak accuracy of 83.57% and converges to 83.36%, compared with 81.98% for FedMCCS and 80.38% for DSCS.