###### Abstract  
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awareE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS. federated learning; client selection; model aggregation; semi-synchronous; IoT 1 202313522023330  
machine-learning models by enabling different IoT devices to analyze their data locally without uploading them to a central facility [5]. A typical example of using FL is to train a next-word prediction model, which is used to predict what word comes next based on the existing text information [6]. Basically, as shown in Figure 1, an FL server would first initialize the parameters of the global model and then broadcast the global model to all the clients via wireless networks. Each client would train the received global model based on its local data sets (i.e., their text messages) and upload the updated model to the FL server via wireless networks. The FL server then aggregates the received models from the clients to generate a new global model and then starts a new iteration by broadcasting the new global model to the clients. The iteration continues until the model is converged. The traditional FL method can resolve the privacy issue to allow the clients to train the model locally, and it applies the synchronous strategy, where the FL server must wait until it receives the models from all the clients in each global iteration. This may lead to the straggler problem when the configurations of clients are heterogeneous, meaning that they have different computing and communications capabilities. Hence, some stragglers take much longer time to train and upload their models in a global iteration because of their lower computing and communications capabilities, and thus significantly prolong the model training process. To resolve the straggler problem, many client selection methods have been proposed [7; 8; 9; 10; 11; 12], which would select the qualified clients that can finish their model training and uploading before a predefined deadline. Normally, client selection and resource allocation are jointly optimized to maximize the number of selected qualified clients. Selecting qualified clients can resolve the straggler problem but may raise other issues. First, the proposed client selection may significantly reduce the number of participating clients, which may slow down the convergence speed [13], thus leading to longer training latency (which equals the sum of the latency for all the global iterations). Second, the proposed client selection may result in the model overfitting issue caused by the reduction of data diversity, i.e., if the FL server only selects the qualified clients to participate in the model training, then the generated model can only fit the data samples in these qualified clients, but not the non-qualified clients. The model overfitting issue would be compounded if the data samples of the qualified clients are not sufficient [14].