# Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

arXiv: https://arxiv.org/abs/2108.01903 · HTML: https://mlsanity.com/paper/2108.01903/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 4 Aug 2021.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection](https://mlsanity.com/paper/1803.08986/) (2018)
- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)

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

- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Cali3F: Calibrated Fast Fair Federated Recommendation System](https://mlsanity.com/paper/2205.13121/) (2022)
- [Federated Learning: Issues in Medical Application](https://mlsanity.com/paper/2109.00202/) (2021)
- [Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems](https://mlsanity.com/paper/2405.05611/) (2024)
- [Robust Convergence in Federated Learning through Label-wise Clustering](https://mlsanity.com/paper/2112.14244/) (2021)
- [Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review](https://mlsanity.com/paper/2308.13714/) (2023)

## Abstract (arXiv)

While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data. In light of this, Federated Learning has recently been proposed as a solution to train on private data without breach of confidentiality. This conservation of privacy is particularly appealing in the field of healthcare, where patient data is highly confidential. However, many studies have shown that its assumption of Independent and Identically Distributed data is unrealistic for medical data. In this paper, we propose Personalized Federated Cluster Models, a hierarchical clustering-based FL process, to predict Major Depressive Disorder severity from Heart Rate Variability. By allowing clients to receive more personalized model, we address problems caused by non-IID data, showing an accuracy increase in severity prediction. This increase in performance may be sufficient to use Personalized Federated Cluster Models in many existing Federated Learning scenarios.