Advancing Edge Intelligence: Federated and Reinforcement Learning for Smarter Embedded Systems
Doktorsavhandling, 2024
Objective: This thesis focuses on improving the performance of machine learning models in embedded systems by using federated learning and reinforcement learning. The main goal is to develop methods that allow edge devices to work together without sharing raw data, which helps maintain privacy. Another goal is to make these systems more adaptable to dynamic environments, so they can perform better under changing conditions. Additionally, the research seeks to improve the efficiency of communication and computation across devices.
Method: The research uses a mix of case studies, simulations and real-world experiments. Federated learning is applied to allow edge devices to train models without centralizing the data, keeping sensitive information local. Reinforcement learning is used to help devices learn how to make better decisions by interacting with their environment. These two methods are tested in different scenarios to evaluate improvements in model accuracy, resource use, and adaptability.
Results: The results of this thesis highlight significant advancements in federated learning (FL) and reinforcement learning (RL) for embedded systems. A comprehensive literature review identified six key challenges and open research questions in FL, emphasizing the need for efficient communication, scalability, and privacy preservation. Case studies in telecommunications and automotive applications demonstrated that FL, particularly with asynchronous aggregation protocols, improves model performance, reduces communication overhead, and speeds up training in real-time, dynamic environments. Novel algorithms, such as AF-DNDF and deep RL approaches, further enhanced decision-making capabilities and adaptability in applications like autonomous driving and UAV base station deployment for disaster scenarios. The development of frameworks like EdgeFL provided practical solutions to overcome FL's implementation challenges, offering scalable, low-effort alternatives. Overall, the integration of FL and RL into embedded systems resulted in improved model accuracy, resource utilization, and adaptability, making these approaches highly suitable for real-world industrial use cases.
Conclusion: This research advances the field of edge intelligence by providing a practical approach to deploying machine learning models that are scalable, privacy-focused, and adaptive in embedded systems. The work demonstrates clear improvements in performance and offers a foundation for future research, which could explore more complex learning approaches and apply these techniques to a wider range of embedded systems.
Federated Learning
Software Engineering
Machine Learning
Reinforcement Learning
Embedded Systems
Författare
Hongyi Zhang
Software Engineering 1
Engineering Federated Learning Systems: A Literature Review
Lecture Notes in Business Information Processing,;Vol. 407(2021)p. 210-218
Paper i proceeding
Towards Federated Learning: A Case Study in the Telecommunication Domain
Lecture Notes in Business Information Processing,;Vol. 434 LNBIP(2021)p. 238-253
Paper i proceeding
Federated learning systems: Architecture alternatives
Proceedings - Asia-Pacific Software Engineering Conference, APSEC,;Vol. 2020-December(2020)p. 385-394
Paper i proceeding
Real-time end-to-end federated learning: An automotive case study
Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021,;(2021)p. 459-468
Paper i proceeding
AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests
Proceedings - 2021 47th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2021,;(2021)p. 308-315
Paper i proceeding
Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning
Proceedings - 2022 IEEE Future Networks World Forum, FNWF 2022,;(2022)p. 184-189
Paper i proceeding
Deep Reinforcement Learning in a Dynamic Environment: A Case Study in the Telecommunication Industry
Proceedings - 48th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2022,;(2022)p. 68-75
Paper i proceeding
5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-Based Integrated Access and Backhaul
IEEE Transactions on Machine Learning in Communications and Networking ,;Vol. 2(2024)p. 1109-1126
Artikel i vetenskaplig tidskrift
Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework
Information and Software Technology,;Vol. 178(2025)
Artikel i vetenskaplig tidskrift
My research investigates how FL can be effectively engineered for resource-constrained devices, ensuring robust privacy and minimal communication costs. It also explores scalable architectures that optimize performance in large, distributed networks. By integrating FL and RL, this work demonstrates how intelligent systems such as autonomous vehicles and drones can not only collaborate to improve their capabilities but also make real-time decisions in complex scenarios, like navigating traffic or managing 5G network connectivity. This thesis provides a foundation for creating smarter embedded systems that prioritize privacy, adaptability, and efficiency. From safer, more autonomous vehicles to highly responsive network systems, the findings highlight the transformative potential of combining Federated and Reinforcement Learning in real-world applications.
Software Engineering for AI/ML/DL
Chalmers AI-forskningscentrum (CHAIR), 2019-11-01 -- 2022-11-01.
Infrastruktur
C3SE (Chalmers Centre for Computational Science and Engineering)
Ämneskategorier
Programvaruteknik
ISBN
978-91-8103-143-0
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5601
Utgivare
Chalmers
Mötesrum 473, Campus Lindholmen. Building Jupiter. Entrance from Hörselgången 5. Go to floor 4.
Opponent: Xavier Franch, Professor Informatics, UPC Universitat Politècnica de Catalunya, Spain