Paper 2025/1515

Privacy-Preserving Federated Inference for Genomic Analysis with Homomorphic Encryption

Abstract

In recent years, federated learning has gained significant momentum as a collaborative machine learning approach, particularly in the field of medicine. While the decentralized nature of federated learning provides greater security guarantees compared to traditional machine learning methods, it is still susceptible to myriad attacks. Moreover, as federated learning becomes increasingly ubiquitous in medicine, its use for classification tasks is expected to increase; however, maintaining patient data confidentiality remains a significant challenge, especially for genetic data. While previous works focus on utilizing Single Nucleotide Polymorphisms or numerical data, recent advances in gene sequencing amplify the importance of inference from DNA sequences. In this work, we introduce a novel framework for secure federated inference on nucleotide-based genotype data and provide a gateway to private inference through fully homomorphic encryption. A federated model with five local clients was created and trained before being encrypted with the TFHE cryptosystem and placed for inference. We tested multiple different local and global model architectures, including a standard neural network, a support vector machine, and an LSTM (long-short-term memory), and compared their respective abilities across accuracy, precision, recall, and runtime metrics. These frameworks successfully identified promoter sequences encoded within given DNA sequences, as well as whether a given sequence was a coding sequence or an intergenomic sequence, showing their potential applications in secure genomic data analysis in a federated context. Our work represents a crucial step in privacy-preserving federated inference on nucleotide-based data.