Federated learning: A rescue in machine learning for data privacy preservation
Abstract
In the era of digitalization, machine learning (ML) has experienced a prodigious growth. Researchers have unfolded the many aspects and issues of ML. The main concern in question while implementing ML is privacy protection. Federated learning (FL) has materialized as a rescue in the case; it is a setup for training the ML algorithms or models in distributed environment without breaching the privacy. The primary goal of this study is to analyze the different aspects of FL undertaken by the researchers. The study will explore the different application aspects of FL.
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