# EPIC: Enhancing Privacy through Iterative Collaboration

arXiv: https://arxiv.org/abs/2411.05167 · HTML: https://mlsanity.com/paper/2411.05167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Experiments of Federated Learning for COVID-19 Chest X-ray Images](https://mlsanity.com/paper/2007.05592/) (2020)
- [On Bridging Generic and Personalized Federated Learning for Image Classification](https://mlsanity.com/paper/2107.00778/) (2021)
- [Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning](https://mlsanity.com/paper/2302.08688/) (2023)
- [Exploring The Potential Of GANs In Biological Sequence Analysis](https://mlsanity.com/paper/2303.02421/) (2023)
- [Anderson Acceleration For Bioinformatics-Based Machine Learning](https://mlsanity.com/paper/2302.00347/) (2023)

## Abstract (arXiv)

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.