Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLim, Bryan Wei Yangen_US
dc.identifier.citationLim, B. W. Y. (2022). Resource allocation for federated learning enabled edge intelligence. Doctoral thesis, Nanyang Technological University, Singapore.
dc.description.abstractThe confluence of Edge Computing and Artificial Intelligence (AI) has driven the rise of Edge Intelligence, which leverages the storage, communication, and computation capabilities of end devices and edge servers to empower AI implementation at scale closer to where data is generated. An enabling technology of Edge Intelligence is the privacy-preserving machine learning paradigm known as Federated Learning (FL). In FL, end users carry out model training locally before transmitting the model parameters or gradient updates, rather than the raw data, to a model owner for aggregation. Amid the increasingly stringent privacy regulations, FL has enabled the development of applications that have to be built using sensitive user data and will continue to revolutionize service delivery in the Finance, Internet of Things (IoT), healthcare, and transport industries, among others. However, the implementation of FL is envisioned to involve thousands of heterogeneous distributed end devices that differ in terms of communication and computation resources, as well as the levels of willingness to participate in the collaborative model training process. The potential node failures, device dropouts, and stragglers effect are key bottlenecks that impede the effective, sustainable, and scalable implementation of FL. In this thesis, I will first present a tutorial and survey on FL and highlight its role in enabling Edge Intelligence. This tutorial and survey provide readers with a comprehensive introduction to the forefront challenges and state-of-the-art approaches towards implementing FL at the edge. In consideration of resource heterogeneity, I then provide multifaceted solutions towards improving the efficiency of resource allocation for implementing FL at scale amid information asymmetry. In the first study, I propose a vanilla contract-theoretic optimization approach towards balancing the tradeoffs of information freshness and service latency in a federated crowdsensing scenario. In the second study, I devise a multi-dimensional contract-matching approach for optimized resource allocation amid multiple sources of heterogeneities. The first two studies leverage the self-revealing properties of contract theory to solve the resource allocation problem in the information asymmetric edge network. Through performance evaluation, it is shown that even when the worker types are unknown, the resource allocation in the edge network is optimized. In the third study, in face of bounded rationality and dynamic decisions of the workers, I propose a two-level evolutionary game theoretic and auction approach to allocate and price resources to facilitate efficient edge intelligence. The performance evaluation shows the convergence and uniqueness of solutions, as well as the profit maximization aspect of the proposed solution. The studies presented in the thesis are formulated via the interdisciplinary interplay of concepts derived from network economics, optimization, game theory, and machine learning. Finally, I outline promising research directions for future works.en_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleResource allocation for federated learning enabled edge intelligenceen_US
dc.typeThesis-Doctor of Philosophyen_US
dc.contributor.supervisorMiao Chun Yanen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.description.degreeDoctor of Philosophyen_US
dc.contributor.researchAlibaba-NTU Joint Research Instituteen_US
item.fulltextWith Fulltext-
Appears in Collections:IGS Theses
Files in This Item:
File Description SizeFormat 
thesis_template_ntu_master (11).pdf5.38 MBAdobe PDFThumbnail

Page view(s)

Updated on Sep 27, 2023

Download(s) 50

Updated on Sep 27, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.