FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform

Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case

“…In the future, integration with multiple technologies will be imperative in order to accomplish multiple tasks or to overcome FL performance issues. [42], analytics of medical data [43], disease monitoring [44], QoS enhancement of FL-based systems [45] IoMT Medical DSS for tracking COVID-19, collaboration among medical institutes [46] Transfer learning Classifying COVID-19 from lung scans, breast cancer classification [47] Access control To safeguard medical data in BDE SMC Construction of virus vulnerability map, data protection in dynamic scenarios [49] Watermarking Privacy protection of gradient information, privacy-preserved data sharing [50] Knowledge distillation Identifying normality, COVID-19, and pneumonia from X-rays, medical image segmentation [51] ZKPs Mobile healthcare, sensitive data protection [52] Split learning Collaborative healthcare analytics [53] Fog computing Performance enhancement of medical devices [54] ChatGPT Knowledge enhancement and better QoS [55,56] Abbreviations: DP = differential privacy, HE = homomorphic encryption, CC = cloud computing, QoS = quality of service, EWS = early warning system, IoT = Internet of things, RL = reinforcement learning, IIoT = industrial Internet of things, NAS = neural architecture search, IoMT = Internet of medical things, DSS = decision support system, BDE = big data environments, SMC = secure multiparty computation, ZKPs = zero-knowledge proofs.…”