Advancing Autism Diagnosis: A Comprehensive Survey of Federated Learning Applications in Healthcare

Challenges occurred at social interaction, behaviour analysis, problems occurred in communication are calculated and characterized by a range, where complex neuro-developmental condition is identified as Autism Spectrum Disorder (ASD). To overcome the ASD problem, early diagnosis, training and intervention are most essential factors that concentrates on improving the quality of life and developmental outcomes of the individual. Clinical observations and subjective assessments are the traditional diagnostic methods, which can be time intensive and has major chances of delay, impacts in the identification and development of those affected. The healthcare majorly opened new avenues for enhancing ASD diagnostics, the data is stored in the form of datasets, where AI models are implemented to detect patterns and subtle indicators of ASD that may not be immediately evident to clinicians, paving the way for faster, more accurate diagnoses. The most challenging factor is implementation of artificial intelligence in ASD diagnosis, as the sensitivity of medical data and strict privacy regulations made the task more difficult. Federated Learning (FL) has performed as furthermost acceptable solution to the challenges that are enabling decentralized artificial intelligence methodologies that are part of artificial intelligence, while protecting the data privacy autism patient data, which can be taken from multiple servers that are distributed across operating devices. As part of experimental analysis, we taken support vector machine of machine learning to implement on the dataset, where ensemble learning methods are discussed as part of proposed methodologies, where we explore the potential of Federated Learning to enhance the ASD diagnosis accuracy and to mitigate long-term effects through early intervention. We done practical analysis of adopting FL in healthcare, as part of fundamental principles. Finally, we discuss lessons learned, unresolved challenges and future directions in FL applications for autism by classifying ASD based on behavioural and biomedical data. Our findings underscore FL’s potential to revolutionize ASD screening and diagnosis, paving the way for more accurate and privacy-conscious healthcare solutions.