# FedStack: Personalized activity monitoring using stacked federated learning

arXiv: https://arxiv.org/abs/2209.13080 · HTML: https://mlsanity.com/paper/2209.13080/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches](https://mlsanity.com/paper/1609.06354/) (2016)
- [Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values](https://mlsanity.com/paper/2005.11627/) (2020)
- [A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison](https://mlsanity.com/paper/2110.10223/) (2021)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Semi-supervised Federated Learning for Activity Recognition](https://mlsanity.com/paper/2011.00851/) (2020)

## Built on by (most cited first, 15 of 15)

- [FedTP: Federated Learning by Transformer Personalization](https://mlsanity.com/paper/2211.01572/) (2022)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning](https://mlsanity.com/paper/2309.10283/) (2023)
- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Sentiment analysis and opinion mining on educational data: A survey](https://mlsanity.com/paper/2302.04359/) (2023)
- [Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence](https://mlsanity.com/paper/2309.10186/) (2023)
- [QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems](https://mlsanity.com/paper/2309.10293/) (2023)
- [Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion](https://mlsanity.com/paper/2309.11044/) (2023)
- [CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics](https://mlsanity.com/paper/2404.19725/) (2024)
- [Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review](https://mlsanity.com/paper/2405.13832/) (2024)
- [FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions](https://mlsanity.com/paper/2408.13926/) (2024)
- [Cross-Fusion Rule for Personalized Federated Learning](https://mlsanity.com/paper/2302.02531/) (2023)

## Abstract (arXiv)

Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying vital signs and physical movements, which are considered crucial tasks. Federated learning is a relatively new AI technique designed to enhance data privacy by decentralizing traditional machine learning modeling. However, traditional federated learning requires identical architectural models to be trained across the local clients and global servers. This limits global model architecture due to the lack of local models heterogeneity. To overcome this, a novel federated learning architecture, FedStack, which supports ensembling heterogeneous architectural client models was proposed in this study. This work offers a protected privacy system for hospitalized in-patients in a decentralized approach and identifies optimum sensor placement. The proposed architecture was applied to a mobile health sensor benchmark dataset from 10 different subjects to classify 12 routine activities. Three AI models, ANN, CNN, and Bi-LSTM were trained on individual subject data. The federated learning architecture was applied to these models to build local and global models capable of state of the art performances. The local CNN model outperformed ANN and Bi-LSTM models on each subject data. Our proposed work has demonstrated better performance for heterogeneous stacking of the local models compared to homogeneous stacking. This work sets the stage to build an enhanced RPM system that incorporates client privacy to assist with clinical observations for patients in an acute mental health facility and ultimately help to prevent unexpected death.