Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3672 2018 2018 Division of CSE, Karunya Institute of Technology and Sciences, Coimbatore, R. R. Department of computer science and engineering, Alliance University, Bangalore, India Saswati Debnath Division of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India Priyadharsini Priyadharsini Division of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India R. Venkatesan Identifying Autism early in children is vital for ensuring more precise developmental support and effective therapeutic interventions. Traditional diagnostic approaches are frequently delayed, and data privacy concerns limit the availability of broad, multi-institutional datasets required for effective machine learning models. To address these limitations, this study proposes a CNN-LSTM-based autism detection model for children using Federated Learning (FL). In the model, temporal and spatial information is extracted from the facial CNNs are highly adept at using convolutional filters to extract spatial features from images. LSTM networks are a specific type of Recurrent Neural Network (RNN) that is ideal for processing time-series or sequences because it can identify long-term relationships in sequential data. This architecture uses CNN layers to extract spatial information from important indications that are important for detecting ASD, like eye patterns, gestures, and facial expressions. After that, these features are sent to LSTM layers, which examine the time-dependent and sequential behavioral patterns associated with autism. Federated Learning allows the locally to train the model on its own dataset locally, sharing only model updates with a central server, thereby preserving data privacy while promoting diverse data contributions. According to experimental results using the proposed techniques, the federated CNN-LSTM model performs 4.3% better than the conventional centralized models because it has less overfitting and is more resilient to a range of data distributions. The model’s performance metrics further highlight its reliability, accuracy, precision, recall, and F1-Score values reaching 98.90%, 97.80%, 98.05%, and 98%, respectively, showing its potential for reliable ASD detection in children across diverse populations.   2025 2025 211 223 10.54216/FPA.190216 https://www.americaspg.com/articleinfo/3/show/3672