Open problems in medical federated learning

Abstract

Purpose This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment. Design/methodology/approach Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions. Findings This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution. Originality/value Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

Federated Learning for Healthcare: A Comprehensive Review

“…The work will help researchers develop a practical federated learning environment for the healthcare industry by taking a novel approach based on modern machine learning methods. Rather than directly sharing datasets with one another, the results of training obtained from multiple centers are connected to make a consolidated overall model [23,24]. In this way, sensitive patient data is protected as it is transferred between hospitals.…”

Cutting-Edge Technologies for Digital Therapeutics: A Review and Architecture Proposals for Future Directions

“…Since most DTx products are mobile software, the first consideration must be understanding the different hardware devices used by each user. To apply digital therapeutics, it is necessary to use a hospital-to-patient structure for federated learning, as shown in Figure 3, among the structures presented in the previous research [39]. Yet, efficient data processing through machine learning becomes problematic since it uses a mobile platform.…”

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

“…6G is mainly motivated by the vision of interconnected intelligence and AI-enabled networks [ 10 ]. Nevertheless, the union of AI and 6G is double-edged.…”

“…However, most of the existing machine learning methods stem from other fields like image recognition and natural language processing. As a result, many technical issues are to be addressed before machine learning efficiently fits into the 6G applications [ 10 ].…”