Muhammad Habib ur Rehman - Federated Learning

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Businesses and Governments are collecting massive data about their customers and citizens. These organizations store the data in centralized cloud storage infrastructures to perform large-scale training to make complex and timely decisions. This data collection process remains continuous; however, customers and citizens always show their concerns about the data management and processing. Considering this, several regulatory measures were taken and various laws were approved by Governments such as the European Union (GDPR), the United States of America (HIPAA/CCPA), and Canada (PIPDEA). However, still, the customers and citizens are raising concerns over their privacy protection and false decisions made by machine learning systems. Therefore, the inability to collect fresh data poses a serious threat to well-informed and realistic decisions.

Google introduced the term federated learning (FL) to enable the machine learning models to be initially trained at the customers or citizens devices and systems and later the model updates are aggregated at the centralized cloud servers. Considering this notion of FL, a large plethora of research activities has been performed by researchers and practitioners in academia and industry. Hence, numerous research publications were produced to solve the active research issues in terms of privacy, security, data and model synchronization, model development and deployment, personalization, incentivization, and heterogeneity across the FL systems.

This book aims to study the FL ecosystem with a broader perspective to cover the theoretical as well as applied aspects of FL systems. Therefore, this book is structured into eight chapters in total. In the first chapter, Ali et al. performed a thorough bibliometric analysis of the field of FL. Authors have conducted thorough research of the Scopus database to uncover the publication trends. They found 476 scholarly documents in total and then analyzed the dataset to find the growth trends in FL research. Also, they studied subject areas and ranked them in terms of the number of publications. Also, they outlined the top-10 cited papers, top-10 authors, top-10 institutions, and top-10 countries. Moreover, they categorized the documents into various types and then uncovered the top-10 sources of these documents. Finally, the authors have performed the domain profiling of the FL research area and they identified five hot domains such as the internet of things (IoT), wireless communication, privacy and security, data analytics, and learning and optimization, where most of the FL research has been creating impact. Christopher et al., in Chap. , review FL as an approach for performing machine learning on distributed data to protect the privacy of user-generated data. They highlight pertinent challenges in an IoT context such as reducing communication costs associated with data transmission, learning from data under heterogeneous conditions, and applying additional privacy protections to FL. Throughout this review, they identify the strengths and weaknesses of different methods applied to FL, and finally, they outline future directions for privacy-preserving FL research, particularly focusing on IoT applications.

To effectively prevent information leakage, K. Wei et al. (in Chap. ) investigate a differential privacy mechanism in which, at the clients side, artificial noises are added to parameters before uploading. Moreover, they propose a K-client random scheduling policy, in which K clients are randomly selected from a total of N clients to participate in each communication round. Furthermore, a theoretical convergence bound is derived from the loss function of the trained FL model. In detail, considering a fixed privacy level, the theoretical bound reveals that there exist an optimal number of clients