Cancer-associated cachexia and pancreatic ductal adenocarcinoma (PDAC) represent two significant challenges in oncology. Cachexia, a severe syndrome characterized by weight loss and muscle atrophy, dramatically worsens patient outcomes across various cancers. Early detection of cachexia is essential for improving quality of life and survival rates. Concurrently, PDAC is one of the deadliest forms of cancer, often arising from intraductal papillary mucinous neoplasms (IPMNs), which are pancreatic cysts with malignant potential. Accurate risk stratification of IPMNs is crucial for early intervention. Both conditions necessitate advanced predictive models that leverage diverse data sources while ensuring patient privacy. The overarching goal of this proposal is to develop and validate privacy-preserving multimodal machine/deep learning (ML/DL) models for the early detection of cachexia across multiple cancer types and risk stratification of PDAC from IPMNs using clinical, imaging, genetic mutational, and social determinants of health (SDOH) data. To access large-scale representative data while respecting the privacy and the security of sensitive patient data, this proposal leverages Federated Learning (FL). This advanced ML paradigm enables the development of robust predictive models across multiple institutions without centralizing data. FL allows each participating institution to train models locally on their own datasets, sharing only model parameters (e.g., weights and gradients) with a central server. This approach maintains data confidentiality and complies with privacy regulations such as HIPAA and GDPR. By aggregating model updates from multiple sites, FL enhances the diversity and generalizability of the models, reducing biases and improving performance across different populations. Aim 1: Develop a multimodal artificial intelligence (AI) model for the early detection of cachexia in cancer patients using clinical data, imaging biomarkers from abdominal computed tomography (CT) scans, and SDOH for pancreatic, colorectal, gastrointestinal, and ovarian cancers. Aim 2: Develop a multimodal predictive model for risk stratification of IPMNs to identify those most likely to progress to invasive pancreatic malignancy using imaging, clinical, patient-specific risk factors, SDOH, histopathology, and genetic mutational data. The federated model’s performance will be benchmarked between sites and the centralized model using F1 score, AUROC, and accuracy metrics. The successful completion of these aims will enhance the early detection of cachexia and risk stratification for PDAC, ultimately leading to better patient outcomes. We are fully prepared to comply with all the administrative requirements outlined in the FOA. Our team has deployed NVIDIA-FLARE and OpenFL with PyTorch and NumPy on Moffitt’s AI cluster and is conducting FL studies. Our NVIDIA-FLARE environment will support FL model training as client and server.