Abstract
•Data security is becoming a more sensitive and important challenge with the increase of AI.•FL provides secure models with no data sharing that leads to a highly efficient privacy-preserving solution.•The main focus of FL is on the image processing applications which ensure that data trained on the model are secure and protected.•FL is concerned about the communication between edge devices and the server and tries to minimize.•Frameworks are discussed along with their usage in federated learning.
Nowadays, data privacy is an important consideration in machine learning. This paper provides an overview of how Federated Learning can be used to improve data security and privacy. Federated Learning is made up of three distinct architectures that ensure that privacy is never jeopardised. Federated learning is a type of collective learning in which individual edge devices are trained and then aggregated on the server without sharing edge device data. On the other hand, federated learning provides secure models with no data sharing, resulting in a highly efficient privacy-preserving solution that also provides security and data access. We discuss the various frameworks used in federated learning, as well as how federated learning is used with machine learning, deep learning, and datamining. This paper focuses on image processing applications that ensure that data trained on the model is secure and protected. We provide a comprehensive overview of the key issues raised in recent literature, as well as an accurate description of the related research work.
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