EPIC: Enhancing Privacy through Iterative Collaboration

TL;DR

EPIC introduces an innovative federated learning architecture that enhances privacy in genomic data analysis by enabling collaborative model training without sharing raw data, specifically applied to SARS-CoV-2 sequence classification.

Contribution

The paper presents a novel privacy-preserving federated learning framework called EPIC, tailored for genomic data, improving collaboration while maintaining data privacy.

Findings

Privacy-preserving strategies do not significantly affect learning convergence.

EPIC effectively classifies SARS-CoV-2 genomic sequences without raw data transfer.

The approach addresses privacy and regulatory challenges in healthcare data sharing.

Abstract

Advancements in genomics technology lead to a rising volume of viral (e.g., SARS-CoV-2) sequence data, resulting in increased usage of machine learning (ML) in bioinformatics. Traditional ML techniques require centralized data collection and processing, posing challenges in realistic healthcare scenarios. Additionally, privacy, ownership, and stringent regulation issues exist when pooling medical data into centralized storage to train a powerful deep learning (DL) model. The Federated learning (FL) approach overcomes such issues by setting up a central aggregator server and a shared global model. It also facilitates data privacy by extracting knowledge while keeping the actual data private. This work proposes a cutting-edge Privacy enhancement through Iterative Collaboration (EPIC) architecture. The network is divided and distributed between local and centralized servers. We demonstrate the EPIC approach to resolve a supervised classification problem to estimate SARS-CoV-2 genomic sequence data lineage without explicitly transferring raw sequence data. We aim to create a universal decentralized optimization framework that allows various data holders to work together and converge to a single predictive model. The findings demonstrate that privacy-preserving strategies can be successfully used with aggregation approaches without materially altering the degree of learning convergence. Finally, we highlight a few potential issues and prospects for study in FL-based approaches to healthcare applications.