Designing <scp>AI</scp> ‐Driven Load Distribution Framework for Federated Networks on Electronic Health Records Using Adaptive Temporal Convolution Network With Attention Mechanism

ABSTRACT The vast amount of electronic health records (EHRs) is a norm to specify the health data in a digital world. As the amount of data starts to grow, handling the tasks on the basis of attributes such as historical performance, computational capacity, and workload results in a serious problem. The high processing loads can consume more resources and result in inefficiencies or delays in using and evaluating sensitive health data. With the emergence of the internet of things (IoT), artificial intelligence (AI), machine learning, and deep learning strategies, data‐driven applications become a promising solution for designing robust diagnostic approaches utilizing healthcare data. These mechanisms obtained much attention from industries and the educational sectors and resulted in better advancements in healthcare models. The AI‐driven approaches associated with healthcare applications still encounter some problems including security, privacy, and the quality‐of‐service (QoS). Machine learning with privacy‐preserving approaches safeguards the data; yet still, it is complex to formulate the infrastructural facilities. Certain problems are efficiently resolved by federated learning (FL) since it enables personal communication between distinct technologies and lightens the system's computational overload. Moreover, it efficiently manages the EHR data. Therefore, this work designs an efficient AI‐driven load distribution in the federated network by considering the limitations of the existing mechanisms. In the developed framework, at first, the essential EHRs are collected from the benchmark resources and provided to the FL network to resolve the overloading issues. Further, the EHR data is used to detect the disease using an Adaptive Temporal Convolution Network with Attention Mechanism (ATCN‐AM) in the FL network. Moreover, the parameters in the ATCN‐AM model are tuned using Improved Dung Beetle Optimizer (IDBO). Finally, different experimental validations are performed in the developed framework over the conventional mechanisms.