Security Model and Algorithms in Federate learning under IoT Critical Infrastructure

Abstract in today's digital world, IoT has a vital and very important role in offering and exchanging information services throughout the internet. it is new technologies and applications that have a deep effect on every domain of our daily life such as education, social, healthcare, and so on. IoT deals with many different sensing objects and machines that interact with each other to do their activities, IoT collects and exchanges data from all sensing objects and machines. Stores the collected data in structured or unstructured format managed through cloud platform services. Due to IoT have classical centralized learning algorithms, it affects directly by computational power and time, most importantly security and privacy issues respect with to user data. So for overcoming these challenges, federated learning is a favorable solution that enables on-object machine learning without migrating end-user private data to the central point (cloud), as well as Artificial Intelligent (AI) benefits to domains with sensitive data, only learning model update will be transferred between end-devices and the central server. By using the federated learning model the scope of the decentralized approach will be boost by having a huge number of clients for collaborating training, this FL makes vulnerable again various security threats and risks. In this paper, we analyze the overall vulnerabilities being exploited by malicious attackers which impact system security and privacy violation, common security threats and attacks in FL to distributed machine learning solutions and defensive techniques to enhance security/privacy vulnerabilities such as security models and algorithms to secure information in a decentralized system, network security as well as access control for availability, integrity, and confidentiality ( AIC), end device are required to protect and secured from unauthorized access.