ABSTRACT
The most significant advancement in this field can be attributed to federated learning (FL). Such a technique permits the classifier to be trained at different locations with a variation of privacy layers featured. It delves into basic Fuzzy Logic ideas viz., its concepts, framework basics, and clustering by data distribution patterns. Various issues such as privacy and security problems, particular needs that result from heterogeneity of data, and security attacks are highlighted broadly in addition with the solutions and methods presented like differential privacy and homomorphic encryption [2]. Besides that, the paper also executes various FL platforms from the standpoint of healthcare, internet of things, autonomous vehicles, recommendation system, and edge computing in order to verify its capacity to redesign it all together in the centralized environment. The emerging- martial field of AI in Florida is full of potential paths to innovation concurrently with data and privacy regulations.
