Federated Learning Aggregation (FLAg) Strategies in Healthcare: A Systematic Survey and Taxonomy Based on Technical and Data Challenges

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Federated learning (FL) has emerged as a promising novel paradigm for enabling multi-institutional collaborations and expediting data access for training and benchmarking Healthcare AI models, without the need for data centralization. During FL, each collaborator performs local computations on their own data and sends model updates to a central server, which collects and aggregates all model updates into a global consensus model. Several FL aggregation (FLAg) strategies have been developed, each focusing on improving the effectiveness of FL, while addressing distinct technical and data-related challenges – from handling varying resource environments, to patient privacy, and to data imbalance across collaborators. In this article, we provide a systematic survey and taxonomy of FLAg strategies designed for healthcare, paired with their relation to known FL challenges, in our attempt to guide readers navigate and interpret the current scattered body of relevant literature. Our analysis reveals that significant progress has been made in improving model utility and privacy guarantees, while mitigating stragglers, system heterogeneity, fairness and biases, uncertainty, security, and data imbalance. We also touch upon current research gaps between current methodological advances and real-world deployment in healthcare, including limitations in i) evaluation beyond model utility, ii) clinical risk assessment, and iii) governance requirements for transparent and accountable FL.