A systematic review of the foundations, applications, and challenges of federated learning.

Federated Learning (FL) is an innovative machine learning paradigm that tackles the challenge of data island while safeguarding data privacy. It enables decentralized model training by allowing multiple clients—such as mobile devices, institutions, or organizations—to collaboratively build models without transferring local data to a central server. This paradigm gained significant attention following Google’s 2016 initiative to predict user text input on Android devices while maintaining the privacy of locally stored data. A core feature of FL is its distributed and encrypted framework, enabling participants to contribute to a collective learning process without revealing their original data to a central entity or other participants. In recent years, FL has evolved to encompass a broader spectrum of decentralized machine learning techniques, while still maintaining privacy as a central tenet. This evolution has positioned FL as a critical technology in sectors where data privacy, security, and sovereignty are paramount. This paper presents a systematic review of the literature on federated learning, synthesizing insights from review articles, Books, key documents, and published research. The review is structured as follows:Overview of Federated Learning: This section introduces the foundational concepts of FL, detailing its origins, core principles, and operational processes. The decentralized structure and privacy-preserving techniques employed in FL are examined, along with real-world applications as examples. Algorithms and Evolution: This section explores the state-of-the-art algorithms driving FL and traces their development over time. Key innovations in aggregation techniques, optimization methods, and client-server communication protocols are highlighted, demonstrating how they have enhanced FL's scalability and efficiency. Classification and Applications of FL Architectures: Federated learning architectures are categorized into three main types: horizontal federated learning, vertical federated learning, and federated transfer learning. This section analyzes the application of these architectures across various domains, highlighting their distinctive features and associated challenges. Applications in IoT, Smart Cities, and Healthcare: Using selected case studies, this section evaluates the deployment of FL in the Internet of Things (IoT), smart cities, and healthcare. It assesses how FL enhances data privacy, security, and operational efficiency in these domains, focusing on practical implementations. Comparative Analysis: This section offers a comparative evaluation of the various methods and algorithms used in the aforementioned fields, identifying their relative strengths and weaknesses. Special attention is given to the challenges posed by large-scale FL deployments, including communication overhead, data heterogeneity, and model convergence. Federated Learning and Related Technologies: This section explores the integration of FL with related technologies, such as federated deep learning and federated blockchain, particularly within the context of the Industrial Internet of Things (IIoT). The potential of these technologies to improve storage, data management, and resource optimization is discussed in detail. Challenges and Future Directions: The final section addresses the ongoing challenges facing FL, including scalability, model accuracy, communication costs, and compliance with regulatory frameworks. Additionally, it proposes future research directions aimed at improving the practicality and widespread adoption of FL in industrial and commercial applications. This systematic review provides a comprehensive examination of federated learning’s current state, including its foundational concepts, applications, and challenges. It also outlines a forward-looking perspective on the advancements needed to establish FL as a key technology in privacy-centric, decentralized machine learning.