Role of federated learning in healthcare systems: A survey

Abstract

Nowadays, machine learning affects practically every industry, but the effectiveness of these systems depends on the accessibility of training data sets. Every device now produces data, and that data can serve as the foundation for upcoming technologies. Traditional machine learning systems need centralised data for their training, but the availability of valid and good amounts of data is not always possible due to various privacy risks. But federated learning can solve this issue [78]. In a federated learning (FL) environment, a model can be trained on decentralised datasets by involving a large number of participants, such as mobile devices or entire enterprises. Researchers are using this technique in various fields and getting great responses. The importance of using federated learning in the healthcare industry is highlighted in this paper since there is a wealth of data available in hospitals or electronic health records that may be used to train medical systems but cannot be shared due to privacy issues. The main contribution of this paper is to highlight the role of federated learning in the medical field. It also presents a list of frameworks available to implement federated learning models. The paper also listed the evaluation metrics used to check the efficiency of a federated learning model. Broadly used evaluation metrics are accuracy, precision, recall, and F1-score. Open issues for research in this area are also discussed at the end of this paper.

Federated Learning for Predictive Healthcare Analytics: From theory to real world applications

“…Figure 1 illustrates the training of a ML model within FL settings. The FL process [5]has described in algorithm 1. Numerous FL aggregated algorithms are available.…”

“…Few FL-aggregation algorithms are Federated Averaging (FedAvg) [6], Federated Stochastic Gradient Descent (FedSGD) [7], FedMA [8], MHAT (Model-Heterogeneous Aggregation Training) [9], FedADAGRAD, FedADAM, FedYOGI [6], Federated Mediation (FedMed) [10], and Faster adaptive FL algorithm (FAFED) [11] etc. The description of various FL-aggregation algorithms is also available in paper [5]. The main challenge while making an ML model is the use of centralized algorithms that rely on a single data source and suggest a decentralized framework for optimization for cooperative learning without explicitly exchanging raw data.…”