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EPIC: Enhancing Privacy through Iterative Collaboration

Prakash Chourasia, Heramb Lonkar, Sarwan Ali, Murray Patterson

TL;DR

The EPIC approach is demonstrated to resolve a supervised classification problem to estimate SARS-CoV-2 genomic sequence data lineage without explicitly transferring raw sequence data, demonstrating that privacy-preserving strategies can be successfully used with aggregation approaches without materially altering the degree of learning convergence.

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.

EPIC: Enhancing Privacy through Iterative Collaboration

TL;DR

The EPIC approach is demonstrated to resolve a supervised classification problem to estimate SARS-CoV-2 genomic sequence data lineage without explicitly transferring raw sequence data, demonstrating that privacy-preserving strategies can be successfully used with aggregation approaches without materially altering the degree of learning convergence.

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.

Paper Structure

This paper contains 13 sections, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Approach
  4. Architecture and Models
  5. Wasserstein Distance Guided Representation Learning (WDGRL) shen2018wasserstein
  6. Poincaré Embedding nickel2017poincare
  7. Centralized Feed Forward Neural Network
  8. EPIC Model
  9. Algorithm and Workflow
  10. Experimental Setup
  11. Dataset statistics
  12. Results And Discussion
  13. Conclusion

Figures (3)

  • Figure 1: Proposed FL Model Overview (map is taken from dmaps_website).
  • Figure 2: Flow chart for a country collaborating in the FL process
  • Figure 3: Training accuracy and loss curve after comp[letting all iterations of different local and Global models while training the model (Local and Global Models). The figure is best seen in color.