Artificial Intelligence Using Federated Learning

Abstract

Artificial intelligence has significantly advanced recently, penetrating various sectors such as healthcare, finance, and smart technology. A key factor driving this progress is federated learning (FL), an innovative decentralized machine learning approach. FL facilitates training models on diverse datasets without exchanging raw data, promoting collaboration while addressing privacy concerns associated with centralized data repositories. The framework utilizes a global deep-learning model from the central server. This study analyzes recent advancements and identifies several unanswered questions stemming from the expansion of FL research. It delves into privacy issues in the FL landscape, including secure multi-party computation, homomorphic encryption, differential privacy, and stochastic gradient descent. Additionally, it explores the application of artificial intelligence in public education, encompassing adaptive technologies, predictive analytics, and edge computing. Furthermore, it investigates the potential of FL in training models with low-power constraints, such as those in the Internet of Things (IoT). Nevertheless, challenges like communication overhead, interoperability standards, and data security concerns persist. This study underscores the transformative potential of federated learning in shaping the future of AI and emphasizes its crucial role in preserving privacy, enabling collaborative learning, and efficient learning mechanisms. Federated learning has been deployed in various domains, including wireless communications, service delivery, smart personal systems, and healthcare. This chapter charts a course forward by examining current FL challenges such as privacy preservation, communication overhead, low performance, and efforts to establish robust models. Federated learning represents a promising paradigm that aims to revolutionize artificial intelligence by facilitating privacy-preserving collaboration and enabling decentralized learning systems. While it holds significant potential, it also confronts challenges that require attention.