# LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

arXiv: https://arxiv.org/abs/1811.12629 · HTML: https://mlsanity.com/paper/1811.12629/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning](https://mlsanity.com/paper/2101.11203/) (2021)
- [Federated Graph Classification over Non-IID Graphs](https://mlsanity.com/paper/2106.13423/) (2021)
- [FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots](https://mlsanity.com/paper/2101.03705/) (2021)
- [FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems](https://mlsanity.com/paper/2104.07515/) (2021)
- [FedCon: A Contrastive Framework for Federated Semi-Supervised Learning](https://mlsanity.com/paper/2109.04533/) (2021)
- [FedGen: Generalizable Federated Learning for Sequential Data](https://mlsanity.com/paper/2211.01914/) (2022)
- [CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning](https://mlsanity.com/paper/2106.07155/) (2021)
- [FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning](https://mlsanity.com/paper/2109.05612/) (2021)
- [SAGDA: Achieving O(ε-2) Communication Complexity in Federated Min-Max Learning](https://mlsanity.com/paper/2210.00611/) (2022)

## Abstract (arXiv)

Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning, which is a method that sends machine learning algorithms simultaneously to all data sources, trains models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them. One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources. To tackle this problem, we proposed an adaptive boosting method named LoAdaBoost that increases the efficiency of federated machine learning. Using intensive care unit data from hospitals, we investigated the performance of learning in IID and non-IID data distribution scenarios, and showed that the proposed LoAdaBoost method achieved higher predictive accuracy with lower computational complexity than the baseline method.