Results of the summarizing process for the arXiv paper: 2104.07914v1

The Internet of Things (IoT) has become an integral part of our daily lives, with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). However, traditional 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. To address these challenges, Federated Learning (FL) has emerged as a distributed collaborative AI approach that enables AI training at distributed IoT devices without the need for data sharing. In this comprehensive survey, we explore the emerging applications of FL in IoT networks, beginning with an introduction to recent advances in FL and IoT and their integration. We then discuss the potential of FL for enabling a wide range of IoT services such as data sharing, offloading and caching, attack detection, localization, mobile crowdsensing, privacy and security. Additionally, we provide an extensive survey on the use of FL in various key IoT applications such as smart healthcare systems, smart transportation systems Unmanned Aerial Vehicles (UAVs), smart cities and smart industry. The survey includes taxonomy tables and key lessons learned to provide insights into FL-IoT integration. Limitations of each FL approach used in IoT are also provided. From the survey of FL-IoT services and applications discussed throughout this paper key lessons learned are highlighted. Lastly we identify important research challenges such as threats in FL systems' performance issues resource management heterogeneity issues in IoT networks along with possible directions for future research toward realizing full potential. The structure is organized into seven sections: Section II discusses state-of-the-art advancements in FL and IoT; Section III provides an extensive discussion on using FL for important IoT services; Section IV explores opportunities brought by FL across various key IoT applications; Section V summarizes several key lessons learned from this comprehensive survey; Section VI discusses research challenges along with possible directions for future research; finally concluding with Section VII. Overall this survey provides a comprehensive overview of FL-IoT integration highlighting the potentials and challenges associated with this booming area. It is an essential resource for researchers practitioners policymakers interested in understanding the latest developments in FL-IoT integration.

• The Internet of Things (IoT) is an integral part of daily life, with intelligent services and applications powered by artificial intelligence (AI).
• Traditional AI techniques require centralized data collection and processing, which may not be feasible in realistic IoT scenarios due to scalability and privacy concerns.
• Federated Learning (FL) is a distributed collaborative AI approach that enables training at distributed IoT devices without data sharing.
• FL can enable a wide range of IoT services such as data sharing, offloading and caching, attack detection, localization, mobile crowdsensing, privacy and security.
• FL has potential for use in smart healthcare systems, smart transportation systems including Unmanned Aerial Vehicles (UAVs), smart cities and smart industry.
• Key lessons learned from the survey include limitations of each FL approach used in IoT networks.
• Research challenges include threats in FL systems' performance issues resource management heterogeneity issues in IoT networks along with possible directions for future research toward realizing full potential.
• The survey is organized into seven sections: state-of-the-art advancements in FL and IoT; using FL for important IoT services; opportunities brought by FL across various key IoT applications; key lessons learned from the comprehensive survey; research challenges along with possible directions for future research; concluding section.

The Internet of Things and Federated Learning: A Comprehensive Survey

The Internet of Things (IoT) has become an integral part of our daily lives, with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). However, traditional 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. To address these challenges, Federated Learning (FL) has emerged as a distributed collaborative AI approach that enables AI training at distributed IoT devices without the need for data sharing. In this comprehensive survey, we explore the emerging applications of FL in IoT networks.

Introduction to Recent Advances in FL and IoT

Recent advances in both FL and IoT have enabled their integration into various key applications such as smart healthcare systems, smart transportation systems Unmanned Aerial Vehicles (UAVs), smart cities and smart industry. This survey provides an extensive discussion on using FL for important IoT services such as data sharing, offloading and caching, attack detection, localization, mobile crowdsensing, privacy and security. Additionally it includes taxonomy tables along with key lessons learned from this comprehensive survey to provide insights into FL-IoT integration. Limitations of each FL approach used in IoT are also provided.

Potential Applications Enabled By Federated Learning

Federated learning is well suited for enabling a wide range of potential applications across various key areas within the internet of things including:

• Data Sharing: FL can enable secure sharing between multiple parties without compromising user privacy.


• Offloading & Caching: FL can help reduce latency by offloading tasks from cloud servers to local devices.


• Attack Detection: FL can detect malicious attacks on connected devices more quickly than traditional methods.


• Localization: FL can be used to accurately locate objects or people within an environment.


• Mobile Crowdsensing : FL can be used to collect large amounts of sensor data from mobile users while preserving their privacy.

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Applications Across Key Areas Within The Internet Of Things

This section provides an extensive survey on the use of federated learning across various key areas within the internet of things including:

< li >< b >Smart Healthcare Systems : In healthcare settings , federated learning can be used to securely share patient health records among different stakeholders while preserving patient privacy . It can also enable personalized medical treatments based on individual patient characteristics .< br / >

< li >< b >Smart Transportation Systems : Federated learning is well suited for enabling autonomous driving , traffic flow optimization , route planning , vehicle safety monitoring , etc . It allows vehicles to learn collaboratively without exchanging sensitive information about their locations or trajectories .< br / >

< li >< b >Unmanned Aerial Vehicles : In UAVs , federated learning is useful for navigation control , object detection/tracking/classification tasks , etc . It allows UAVs to collaborate with each other without revealing sensitive information about their flight paths or payloads .< br /