Smart client selection strategies for enhanced federated learning in digital healthcare applications

• Published in: Multimedia tools and applications Vol. 84; no. 19; pp. 21589 - 21604
• Main Authors: D N, Sachin, B, Annappa, Ambesange, Sateesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2025; Springer Nature B.V
• Subjects: 1239: Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis; Accuracy; Artificial intelligence; Clients; Clinical outcomes; Communication; Computer Communication Networks; Computer Science; Cooperation; Data Structures and Information Theory; Federated learning; Health care; Machine learning; Multimedia Information Systems; Personal health; Privacy; Special Purpose and Application-Based Systems; Uncertainty; Federated learning; Client selection; Distributed computing; Digital healthcare; Edge computing
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-19403-5

Federated Learning (FL) trains AI models in healthcare without sharing patient data. FL computes client models locally and combines them to create a global model. However, involving all clients is impractical due to resource limitations. Random selection of a subset of clients in each FL round can pose challenges for resource-limited devices, leading to longer processing times and potential training failures. To tackle these obstacles, this research proposes a novel strategy for FL that treats each training round as a client selection process to improve the efficiency and effectiveness of FL in healthcare applications, where data privacy is paramount. The approach begins by calculating the uncertainty value for each client, which quantifies the contribution of the client’s data to the overall model. Clients are then ranked based on their uncertainty values, and those with higher loss values are given a higher probability of participating in the training process. The experimental outcomes clearly show that the proposed strategy effectively makes 1.3x training faster, and 30% lowers communication expenses, conserves computational resources, and enhances model performance when contrasted with random client selection.