Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives

Abstract

Current approaches to developing artificial intelligence (AI) models for widespread glaucoma screening have encountered several obstacles. First, glaucoma is a complex condition with a wide range of morphological and clinical presentations. There exists no consensus definition of glaucoma or glaucomatous optic neuropathy. Further, training effective deep learning algorithms poses numerous challenges, including susceptibility to overfitting and lack of generalizability on external data. Therefore, training data should ideally be sourced from large, well-curated, multi-client cohorts to ensure diversity in patient populations, disease presentations, and imaging protocols. However, the construction of centralized repositories for multimodal data faces hurdles such as concerns regarding data sharing, re-identification, storage, regulations, patient privacy, and intellectual property. Federated learning (FL) has emerged as a proposed solution to address some of these concerns by enabling data to remain locally hosted while facilitating distributed model training. This article aims to provide a comprehensive review of the existing literature on FL in the context of its applications for AI tasks related to glaucoma.