## 1 Introduction
Indeed, with these restrictions in place, the number of training samples would not be large enough to generalize the model, affecting the performance of the trained model. To overcome these challenges, Google in [9; 10] proposed federated learning (FL): a combination of distributed and incremental machine learning. FL is a distributed privacy-preserving machine learning technique that enables the collaborative training of a shared global predictive model without the need of uploading private local data to a central server to overcome the privacy concerns caused by centralized machine learning. The FL algorithm's efficacy can be further improved by introducing feature selection at the distributed nodes. Feature selection will improve the identification of common features set in the sensory health data and distributed over the healthcare registries. Furthermore, feature selection will also help in dimensionality reduction to lower the computational cost and the model size. In this regard, recently, a feature-optimized federated learning-based technique was proposed in [11]; they addressed the issue of dimensionality reduction and communication efficiency for heart disease by improving the distributed nodes' learning technique. For the security and privacy issues in the cloud computing environment, federated learning incentive-based mechanisms were introduced in [12; 13; 14]. Recently, some meta-heuristic techniques have been proposed to further expand the solution search space for cloud-based healthcare systems [15; 16]. These techniques also aim to minimize the fitness (objective) function though preserve the size of the population, increase weight adaptation rates, improve local search techniques, offer fitness function-improved computation, provide solutions to avoid local minima, and enhance the convergence rate of the algorithm. Deep learning (DL) and SVM are both effective methods, although they are made to address distinct challenges. While DL is better suited for big datasets with many features, SVM works well for small to medium-sized datasets with few features. In comparison to DL, SVM is less prone to overfitting and is known to be successful in high-dimensional space. SVM can be trained using various kernel functions, such as linear, polynomial, and radial basis functions (RBF), which can help to handle non-linear data. In tabular datasets, SVM can be used in conjunction with feature scaling, normalization, and dimensionality reduction techniques to improve the performance. Motivated by these highlighted issues of data privacy, improved feature selection, and classification for heart disease, in this manuscript, we proposed a privacy-aware FL-based framework that utilizes federated matched averaging at the HSPs' cloud end with a hybrid technique of modified artificial bee colony with support vector machine (MABC-SVM) for optimization for effective CVD prediction, respectively, at the client nodes. The M-ABC at the HSP client for optimal solution search works in four phases, i.e., the initialization phase, employed bee phase, onlooker bee phase, and the scout bee phase. These steps are described in detail in Section 4.1. The primary contributions of the proposal are enumerated as follows:  
* An FL-based framework is proposed in this paper to overcome the problem of data privacy for HSP systems. * We utilize the modified version of a federated matched averaging (FedMA) algorithm to preserve the privacy of heart disease data and to address the issues of the HSP's central model updation and communication efficiency. * A hybrid technique comprised of a modified artificial bee colony and support vector machine (MABC-SVM) is proposed for the prediction of CVD with improved prediction accuracy. This hybrid algorithm is introduced at the client end of HSP. * Our hybrid method's performance in terms of communication efficiency, classification error, and prediction accuracy is assessed and compared to current FL approaches. The rest of the paper is organized as follows.