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Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive data from medical institutions to data centers that process the fused information. Training on data centers thus requires higher communication resource/energy demands while violating privacy. This is considered today as a significant bottleneck in pursuing scientific collaboration across trans-national clinical medical research centers. Recently, federated learning (FL) has emerged as a distributed AI approach that enables the cooperative training of ML models, without the need of sharing patient data. This paper dives into the analysis of different FL methods and proposes a real-time distributed networking framework based on the …