Abstract
Artificial intelligence (AI)-driven behavior recognition and privacy-preserving machine learning frameworks offer transformative potential for digital behavioral trials. However, multisite longitudinal studies present unique challenges, including incomplete and high-dimensional data, non-normal distributions, evolving behavioral trajectories, and stringent privacy regulations such as HIPAA, where even anonymized datasets may risk re-identification. Soft clustering methods are well-suited for such complex data environments because they enable partial membership and capture overlapping, dynamic behavioral trajectories. When integrated with multiple imputation, they offer a robust approach for addressing missingness in longitudinal digital trial datasets. Despite progress, current approaches lack an efficient, fully integrated framework for soft encoder optimization, cluster validation, and visualization under federated constraints. To overcome these limitations, we propose the Intelligent Multiple Imputation Federated Fuzzy Clustering with Visualization and Validation (iMIF2V2) framework - a decentralized, intelligent, and distribution-free AI model designed to streamline the entire unsupervised clustering pipeline in federated digital health settings. iMIF2V2 unifies adaptive fuzzifier tuning, weighted rank aggregation, and visualization-guided validation within a privacy-preserving federated architecture. Empirical validation was conducted using harmonized longitudinal dietary datasets from four Massachusetts RCTs (n = 957) and two national studies (totaling over 3.3 million observations), alongside extensive simulation experiments that varied the number of clients, clusters, effect sizes, and correlation structures. The algorithm automatically detected optimal cluster numbers and fuzzifiers across studies, converged rapidly, and demonstrated high clustering accuracy, particularly for larger effect sizes and balanced site-level samples. Simulated results confirmed the robustness of the distribution-free design of iMIF2V2 across diverse data distributions and missingness patterns. A federated implementation, deployed across two GPU servers emulating separate clients, demonstrated practical feasibility. The accompanying web interface provides public access for exploratory visualization of local and global centroids, longitudinal trajectories, and optimized 2D/3D Sammon projections. By integrating intelligent fuzzy clustering, multiple imputation, visualization, and federated learning into a unified, streamlined, and privacy-preserving pipeline, iMIF2V2 establishes a scalable foundation for interpretable, reproducible, and secure analysis of multi-site longitudinal behavioral digital trials.