# Differentially Private Federated Learning for Cancer Prediction

arXiv: https://arxiv.org/abs/2101.02997 · HTML: https://mlsanity.com/paper/2101.02997/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [White-box vs Black-box: Bayes Optimal Strategies for Membership Inference](https://mlsanity.com/paper/1908.11229/) (2019)
- [R'enyi Differential Privacy of the Sampled Gaussian Mechanism](https://mlsanity.com/paper/1908.10530/) (2019)
- [Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising](https://mlsanity.com/paper/2007.11524/) (2020)

## Built on by (most cited first, 6 of 6)

- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs](https://mlsanity.com/paper/2402.14527/) (2024)
- [Securing Genomic Data Against Inference Attacks in Federated Learning Environments](https://mlsanity.com/paper/2505.07188/) (2025)
- [A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning](https://mlsanity.com/paper/2603.13293/) (2026)
- [Federated Deep Learning in Electricity Forecasting: An MCDM Approach](https://mlsanity.com/paper/2111.13834/) (2021)
- [A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model](https://mlsanity.com/paper/2202.01907/) (2022)

## Abstract (arXiv)

Since 2014, the NIH funded iDASH (integrating Data for Analysis, Anonymization, SHaring) National Center for Biomedical Computing has hosted yearly competitions on the topic of private computing for genomic data. For one track of the 2020 iteration of this competition, participants were challenged to produce an approach to federated learning (FL) training of genomic cancer prediction models using differential privacy (DP), with submissions ranked according to held-out test accuracy for a given set of DP budgets. More precisely, in this track, we are tasked with training a supervised model for the prediction of breast cancer occurrence from genomic data split between two virtual centers while ensuring data privacy with respect to model transfer via DP. In this article, we present our 3rd place submission to this competition. During the competition, we encountered two main challenges discussed in this article: i) ensuring correctness of the privacy budget evaluation and ii) achieving an acceptable trade-off between prediction performance and privacy budget.