Cloud-Based Federated Learning Implementation Across Medical Centers

Abstract

PURPOSE Building well-performing machine learning (ML) models in health care has always been exigent because of the data-sharing concerns, yet ML approaches often require larger training samples than is afforded by one institution. This paper explores several federated learning implementations by applying them in both a simulated environment and an actual implementation using electronic health record data from two academic medical centers on a Microsoft Azure Cloud Databricks platform. MATERIALS AND METHODS Using two separate cloud tenants, ML models were created, trained, and exchanged from one institution to another via a GitHub repository. Federated learning processes were applied to both artificial neural networks (ANNs) and logistic regression (LR) models on the horizontal data sets that are varying in count and availability. Incremental and cyclic federated learning models have been tested in simulation and real environments. RESULTS The cyclically trained ANN showed a 3% increase in performance, a significant improvement across most attempts ( P < .05). Single weight neural network models showed improvement in some cases. However, LR models did not show much improvement after federated learning processes. The specific process that improved the performance differed based on the ML model and how federated learning was implemented. Moreover, we have confirmed that the order of the institutions during the training did influence the overall performance increase. CONCLUSION Unlike previous studies, our work has shown the implementation and effectiveness of federated learning processes beyond simulation. Additionally, we have identified different federated learning models that have achieved statistically significant performances. More work is needed to achieve effective federated learning processes in biomedicine, while preserving the security and privacy of the data.

A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

“…HFL data partition is quite common in FL applied for medical applications. More than half of FL studies on medical applications implemented horizontal medical data partition in their experiment [18,19,21,37,[39][40][41][42][43][44][45][46][47][48][49]51,52,54,55]. Unlike FL applied for nonmedical applications where training is carried out across many nodes, FL studies in medical applications only handle limited nodes from 2 to 100, as listed in Table 2.…”

“…(1) The healthcare node generates a synthetic sample to balance the training dataset in the local data augmentation method. The synthetic minority oversampling technique (SMOTE) [21,49], generative adversarial method (GAN) [44], or geometric transformation [40,48,53] is employed to generate a synthetic sample in an FL environment.…”

In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

“…Furthermore, distribution discrepancies in training data from these populations result in biases that are one of the major hindrances before generalizing ML approaches. Given the large volume and diverse data needed for model training, Federated Learning (FL) approaches may provide a novel opportunity for the future of ML applications (Rajendran et al, 2021;Sarma et al, 2021). FL is a collaborative ML training approach in which training data is not centralized and stays within organizational boundaries (Figure 1).…”

Proceedings of the Third Workshop on Privacy in Natural Language Processing

“…There have been many recent research works on deploying the FL framework in several applications like image classification , emotion detection (Chhikara et al, 2021), anonymization (Choudhury et al, 2020), robotics (Imteaj and Amini, 2020), etc. and also in medical domain (Rajendran et al, 2021;Kerkouche et al, 2021;Choudhury et al, 2019;Ge et al, 2020). Researchers also investigated the scope of the differentially private algorithm in several applications (Zhao et al, 2020;Koda et al, 2020;Hu et al, 2020;Chen et al, 2018).…”