Blockchain with Federated Learning for Secure Healthcare Applications
Akansha Singh, Krishna Kant Singh · 2023
As big data and artificial intelligence develop, the public's demand for privacy continues to grow. Consequently, the topic of federated learning is raised. It is a fresh approach to cross-platform privacy defense. A workable paradigm for federated learning has been developed. Acknowledged by an increasing number of scholars and businesses today, it places a strong emphasis on data security and privacy. For instance, if users are unable to train appropriate models due to a lack of data, federated learning may combine multiple models without disclosure, and users may upgrade the integrated model. Conversely, when consumers do not have enough federated learning, they may not only supply data labels for learners to learn from but also migrate models, via a safe model sharing method. The fundamental concept and associated technologies are introduced in this work. Then, the general categories of federated learning and the real-world examples of federated learning are addressed, and the present problems and potential research prospects of federated learning are sorted. Federated states are anticipated to exist in the near future. Learning can provide shared security services that are safe for many applications and encourage the steady development of artificial intelligence.