Federated Learning for Internet of Things: A Comprehensive Survey

Abstract: The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by AI. Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

• The paper addresses federated learning's integration with IoT to enable decentralized training and preserve sensitive data.
• It demonstrates how FL optimizes data sharing, offloading, caching, and localization, thereby improving overall system performance.
• The study highlights robust defense mechanisms against cyber threats while outlining future research to mitigate communication overhead and data heterogeneity.

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning (FL) has been gradually emerging as a pivotal distributed AI framework for the effective implementation of Internet of Things (IoT) applications. This paper addresses the application of FL by presenting an encompassing survey of its integration with IoT systems, thereby expanding the boundaries of intelligent IoT applications while ensuring data privacy and reducing latency. The paper delineates various facets of FL, elaborating on how it optimizes IoT data sharing, offloading, caching, attack detection, localization, mobile crowdsensing, and endows IoT systems with enhanced privacy and security features.

Key Contributions and Insights

• IoT Data Sharing and Privacy: Traditional AI approaches require centralized data storage, which poses privacy concerns. By employing FL, mobile and IoT devices collaborate to train models without sharing sensitive datasets. The connection to blockchain technologies underscores advancements in secure and traceable data sharing within industrial IoT networks.
• Data Offloading and Caching: FL-based strategies efficiently tackle IoT data offloading and caching demands through distributed learning at network edges, mitigating resource burdens typically centralized in server architectures. FL augments edge caching systems by predicting and handling popular content without collecting exhaustive user data, thereby limiting privacy risks.
• Attack Detection and Robust Defense Mechanisms: The paper highlights FL's capabilities in safeguarding IoT systems against adversarial threats. The collaborative detection and defense models enhance security layers across industrial environments by coordinating the training of decentralized models, which remain less vulnerable to data leaks and model poisoning attacks.
• Localization Services: The application of FL to localization within IoT networks illustrates its strength in providing effective positioning services. By leveraging FL's architecture, localization accuracy improves, notably in RSS-based fingerprint localization tasks, without compromising user privacy.
• Smart Healthcare: IoT's integration into healthcare requires models that prioritize privacy due to the sensitive nature of Electronic Health Records (EHRs). The survey illustrates how FL empowers healthcare systems to collaboratively train AI models across hospitals while ensuring compliance with privacy regulations.
• Smart Transportation and UAV Management: In vehicular networks, FL facilitates intelligent resource allocation, enabling effective traffic management and ultra-reliable low-latency communication. Moreover, FL's contribution extends to UAV operations, optimizing path control and swarm intelligence through collaborative aerial-ground learning models.
• Smart Cities and Industry 4.0: An exploration into the role of FL within smart cities reveals enhancements in data management and infrastructure monitoring. FL's enduring capacity to maintain industrial privacy while optimizing resource allocation among robotic systems encapsulates its value in Industry 4.0 settings.

Future Directions

Despite significant advances, FL still encounters challenges related to privacy guarantees, communication overhead, and inefficiencies due to non-IID data distributions and device heterogeneities. Future research should focus on building robust models that incorporate advanced cryptographic techniques, optimize communication protocols, and adapt to evolving IoT network dynamics. Unifying FL with emerging technologies like edge computing and integrating blockchain for trust and reliability could further solidify its role as a crucial enabler of next-generation IoT applications.

This paper contributes to the growing body of research dedicated to expanding FL's utilization in IoT, guiding future studies to harness its potential while addressing current limitations. The examination of IoT-specific FL use cases reaffirms the framework's capacity to transform diverse sectors, promoting seamless AI integration in an increasingly interconnected ecosystem.