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Federated Learning for Distributed Reasoning on Edge Computing
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0001-5924-5457
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0003-4208-6757
2021 (English)In: Procedia Computer Science: Proceedings of the XI Latin and American Algorithms, Graphs and Optimization Symposium / [ed] Carlos Eduardo Ferreira; Orlando Lee Flávio; Keidi Miyazawa, Elsevier , 2021, p. 419-427Conference paper, Published paper (Refereed)
Abstract [en]

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site

Place, publisher, year, edition, pages
Elsevier , 2021. p. 419-427
Series
Procedia Computer Science, E-ISSN 1877-0509 ; 184
Keywords [en]
Distributed Reasoning, SDNFederated Learning, Edge Computing, Internet of Things, LSTM, Smart Grid
National Category
Computer Engineering
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-200481DOI: 10.1016/j.procs.2021.03.053OAI: oai:DiVA.org:su-200481DiVA, id: diva2:1625143
Conference
The 12th International Conference on Ambient Systems, Networks and Technologies (ANT), March 23 - 26, 2021, Warsaw, Poland
Available from: 2022-01-05 Created: 2022-01-05 Last updated: 2023-08-30Bibliographically approved
In thesis
1. Distributed Intelligence for IoT Systems Using Edge Computing
Open this publication in new window or tab >>Distributed Intelligence for IoT Systems Using Edge Computing
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the past decade, the Internet of Things (IoT) has undergone a paradigm shift away from centralized cloud computing to edge computing. Hundreds of billions of things are estimated to be deployed in the rapidly advancing IoT paradigm, resulting in an enormous amount of data. Sending all the data to the cloud has recently proven to be a performance bottleneck, as it causes many network issues, including high latency, high power consumption, security issues, privacy issues, etc. However, the existing paradigms do not use edge devices for decision-making. Distributed intelligence could strengthen the IoT in several ways by distributing decision-making tasks among edge devices within the network instead of sending all data to a central server. All computational tasks and data are shared among edge devices. Edge computing offers many advantages, including distributed processing, low latency, fault tolerance, better scalability, better security, and data protection. These advantages are helpful for critical applications that require higher reliability, real-time processing, mobility support, and context awareness. This thesis investigated the application of different types of intelligence (e.g., rule-based, machine learning, etc.) to implementing distributed intelligence at the edge of the network and the network challenges that arise. The first part of this thesis presents a novel and generalizable distributed intelligence architecture that leverages edge computing to enable the intelligence of things by utilizing information closer to IoT devices. The architecture is comprised of two tiers, which address the heterogeneity and constraints of IoT devices. Additionally, the first part of this thesis identifies a suitable reasoner for two-level distributed intelligence and an efficient way of applying it in the architecture via an IoT gateway. To mitigate communication challenges in edge computing, the second part of the thesis proposes two-level mechanisms by leveraging the benefits of software-defined networking (SDN) and 5G networks based on open radio access network (O-RAN) as part of a communication overlay for the distributed intelligence architecture. The third part of this thesis investigates integrating the two-tier architecture and the communication mechanisms in order to provide distributed intelligence in IoT systems in an optimal manner.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2023. p. 70
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 23-008
Keywords
Internet of Things (IoT), Edge Computing, Distributed Intelligence, Software Defined Networking (SDN), Federated Learning, 5G, O-RAN, Network Slicing, Reinforcement Learning
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-220549 (URN)978-91-8014-476-6 (ISBN)978-91-8014-477-3 (ISBN)
Public defence
2023-10-13, Lilla hörsalen, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2023-09-20 Created: 2023-08-30 Last updated: 2023-09-12Bibliographically approved

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