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 hyperparameter 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 hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density‐based clustering and genetic hyperparameter 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. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR‐10 dataset with only 10 training rounds.

Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy‐preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low‐power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL‐KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps.

PURPOSE: Reviewing pathology reports requires physicians to integrate complex histopathologic, immunohistochemical, and molecular findings from multiple reports and institutions, often under time constraints that increase the risk of error and fatigue. Large language models (LLMs) offer a potential solution by generating concise, coherent summaries from complex pathology data. METHODS: Patients who underwent initial consultation in a thoracic clinic between January 2019 and July 2023 were included. Original pathology reports and corresponding physician pathology summaries from consultation notes were extracted and anonymized. Six open-source LLMs (Llama 3.0, Llama 3.1, Llama 3.2, Mistral, Gemma, and DeepSeek-R1) generated pathology summaries directly from the original reports. Objective and subjective evaluations were performed using the original reports as the ground truth. LLM-generated summaries were compared with physician summaries for correctness, completeness, and conciseness. Additional subjective assessments with multiple evaluators were conducted for Llama 3.1. RESULTS: Ninety-four cases met the eligibility criteria. Using the original pathology reports as the ground truth, the LLM-generated summaries achieved higher scores across all objective evaluation metrics compared with physician pathology summaries ( P < .0001). In the subjective evaluation, DeepSeek, Mistral, Llama 3.1, and Llama 3.2 achieved higher ratings for completeness ( P = .017, P < .0001, P < .0001, and P < .0001, respectively) while maintaining comparable correctness relative to physician pathology summaries ( P = 1.000). The results remained consistent in additional subjective analyses involving multiple evaluators for Llama 3.1. CONCLUSION: LLM-generated summaries demonstrated better performance in objective metrics and greater completeness in subjective evaluations compared with physician summaries. These results highlight the potential of LLMs as valuable tools for enhancing clinical documentation and workflow efficiency in oncology practice.