Differentially Private Federated Learning for Genomic-Based Stomach Adenocarcinoma Detection in Consumer Medical IoT

Stomach Adenocarcinoma (STAD) significantly contributes to global cancer mortality, underscoring the urgent need for precise diagnostic methods. Traditionally, artificial intelligence (AI) methods have relied heavily on imaging techniques like CT, PET, and MRI. However, genomic data represents an underutilized resource for identifying STAD-related genetic mutations. This study explores genomic potential by analyzing amino acid sequences of genes impacted by STAD. We propose a framework integrating Huffman Encoding (HMC) for feature extraction and a Differentially Private Federated Long Short-Term Memory (DPFLSTM) model within a federated learning (FL) setting enhanced by differential privacy (DP). The DPFLSTM framework is specifically tailored for consumer-centric Internet of Medical Things (IoMT) environments, facilitating secure collaboration among diverse consumer medical IoT devices. Our DPFLSTM model achieves notable accuracies of 0.93 in testing and 0.99 in training, highlighting a significant improvement in diagnostic precision and data privacy. Additionally, the HMC feature set improves not only DPFLSTM but also conventional ML models (PSVM, RSVM, RF, BNB, DT). This research establishes a new standard for secure and effective genomic diagnostics, promoting multi-institutional collaboration and integration into IoMT-based clinical decision support systems (CDSS).