An Introduction to Federated Learning: Working, Types, Benefits and Limitations

Abstract

Machine learning has been constantly evolving and revolutionizing every aspect of our lives. There is ongoing research to enhance and modify machine learning models where scientists and researchers are finding ways to improve the effectiveness and adaptability of models with the changing technology moulding to user requirements for real life application. The main challenges in this endeavour of enhancing machine learning models are obtaining quality data, selecting an appropriate model, and ensuring the data privacy. Federated learning has been developed to address the aforementioned challenges, which is an effective way to train machine learning models in a collaborative manner by using the local data from a large number of devices without directly exchanging their raw data whilst simultaneously delivering on model performance. Federated learning is not just a type of machine learning, it is an amalgamation of several technologies and techniques. To fully understand its concepts a comprehensive study is required. This paper aims to simplify the fundamentals of federated learning in order to provide a better understanding of it. It explains federated learning in a step-by-step manner covering its comprehensive definition, detailed working, different types, benefits and limitations.