This paper is available on arxiv under CC 4.0 license. Authors: (1) Berrenur Saylam; (2) Ozlem Durmaz ¨ ˙Incel. Table of Links Highlights Abstract & Introduction Federated Learning: Collaborative and Privacy-Preserving Model Training State of the Art Federated Learning Software Platforms and Testbeds Sensors and Edge-Sensing Devices Federated Learning Applications on Sensing Devices Discussion Conclusions, Acknowledgment, and References 7. Conclusion This paper has provided an overview of the current state-of-the-art FL methodologies applied to sensing devices, focusing on IoT sensors, mobile devices, and wearables. By examining the challenges and opportunities in applying FL to sensing tasks, the paper aims to inspire further research in this domain. The discussion section has raised several important points for consideration. It highlighted the need to address the asynchrony in FL, leverage unlabelled on-device data, explore FL applications in Human Activity Recognition (HAR) and affective computing, and adapt FL methodologies from simulation environments to real-world applications on sensing devices. Efficient wireless communication, resource efficiency, and personalization are crucial when applying FL methodologies to sensing devices. Future research should focus on developing wireless communication techniques that minimize data exchange, designing algorithms with resource constraints in mind, and enabling privacy-preserving personalization. By addressing these challenges, FL methodologies can be effectively applied in real-world sensing applications, considering sensing devices’ unique constraints and requirements. Moreover, the discussion emphasizes the need for studies that bridge the gap between simulation and real-world deployment, ensuring the practicality and effectiveness of FL methodologies in real-world sensing applications. By adapting FL methodologies to real-world settings, researchers can address additional challenges, such as communication constraints, privacy concerns, and device heterogeneity, and validate the performance of FL in diverse sensing domains. In conclusion, this paper aims to provide a comprehensive review for researchers and practitioners interested in applying FL methodologies to edge sensing devices to contribute to developing intelligent and efficient systems in diverse application domains by highlighting the specific challenges and opportunities associated with these devices. Acknowledgment T¨ubitak Bideb 2211-A academic reward is gratefully acknowledged. This research has been supported by the Bo˘gazi¸ci University Research Fund, project number: 19301P. 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An Overview of the Current State-of-the-Art Federated Learning Methodologies

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