###### Abstract  
This paper introduces an improved version of the Federated Random High Local Performance (Fed-RHLP) algorithm, specifically aimed at addressing the difficulties posed by Non-IID (Non-Independent and Identically Distributed) data within the context of federated learning. The refined Fed-RHLP algorithm implements a more targeted client selection approach, emphasizing clients based on the size of their datasets, the diversity of labels, and the performance of their local models. It employs a biased roulette wheel mechanism for selecting clients, which improves the aggregation of the global model. This approach ensures that the global model is primarily influenced by high-performing clients while still permitting contributions from those with lower performance during the model training process. Experimental findings indicate that the improved Fed-RHLP algorithm significantly surpasses existing methodologies, including Federated Averaging (FedAvg), Power of Choice (PoC), and FedChoice, by achieving superior global model accuracy, accelerated convergence rates, and decreased execution times, especially under conditions of high Non-IID data. Furthermore, the improved Fed-RHLP algorithm exhibits resilience even when the number of clients participating in local model updates and aggregation is diminished in each communication round. This characteristic positively influences the conservation of limited communication and computational resources. client selection; improved Fed-RHLP algorithm; Non-IID issue; model aggregation 2025181181  
advantages, such as reduced communication costs, improved resource efficiency, and enhanced model generalization [2]. A significant challenge in FL relates to suboptimal client selection and excessive diversity in model aggregation. Client heterogeneity can arise from statistical disparities, such as imbalanced datasets or substantial variations in data volume across clients, which may lead to diminished model accuracy and prolonged convergence times [3]. Furthermore, the non-independent and identically distributed (Non-IID) characteristics of the data compound this challenge. Each client possesses training samples from a limited range of classes and demonstrates imbalanced sample distributions during the training process. The Non-IID challenge can be categorized into two distinct levels: high-level and low-level issues. High-level issues emerge when each client is assigned only a few labels for local model training. Conversely, low-level issues occur when clients have access to a broader range of labels, albeit not the complete set. The challenges associated with heterogeneous client selection and low-quality data are particularly evident in the random client selection mechanism employed by the Federated Averaging algorithm (FedAvg). As previous research has indicated [4], FedAvg may result in suboptimal global model aggregation [5; 6], leading to increased delays and diminished model accuracy, especially when addressing Non-IID data across clients [7; 8; 9]. In response to the issues arising from random client selection, various strategies have been developed that incorporate bias values to select clients based on their number of training samples and elevated local loss. One such strategy, as highlighted in [10], proposed the Power-of-Choice algorithm (PoC), which prioritizes clients exhibiting higher local loss values to enhance convergence rates. Nevertheless, the PoC approach may deviate from the optimal central loss function, potentially compromising overall model accuracy due to its singular focus on accelerating convergence. To refine the PoC methodology, the FedChoice algorithm was introduced [11]. This algorithm aims to improve global model convergence by favoring clients with higher loss values, thereby increasing their likelihood of participating in local model training and subsequent integration into the global model.