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Group-Personalized Federated Learning for Human Activity Recognition Through Cluster Eccentricity Analysis
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-6061-0861
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
2023 (English)In: Engineering Applications of Neural Networks: 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings / [ed] Iliadis L., Maglogiannis I., Alonso S., Jayne C., Pimenidis E., Springer Science+Business Media B.V., 2023, p. 505-519Conference paper, Published paper (Refereed)
Abstract [en]

Human Activity Recognition (HAR) plays a significant role in recent years due to its applications in various fields including health care and well-being. Traditional centralized methods reach very high recognition rates, but they incur privacy and scalability issues. Federated learning (FL) is a leading distributed machine learning (ML) paradigm, to train a global model collaboratively on distributed data in a privacy-preserving manner. However, for HAR scenarios, the existing action recognition system mainly focuses on a unified model, i.e. it does not provide users with personalized recognition of activities. Furthermore, the heterogeneity of data across user devices can lead to degraded performance of traditional FL models in the smart applications such as personalized health care. To this end, we propose a novel federated learning model that tries to cope with a statistically heterogeneous federated learning environment by introducing a group-personalized FL (GP-FL) solution. The proposed GP-FL algorithm builds several global ML models, each one trained iteratively on a dynamic group of clients with homogeneous class probability estimations. The performance of the proposed FL scheme is studied and evaluated on real-world HAR data. The evaluation results demonstrate that our approach has advantages in terms of model performance and convergence speed with respect to two baseline FL algorithms used for comparison. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023. p. 505-519
Series
Communications in Computer and Information Science (CCIS), ISSN 1865-0929, E-ISSN 1865-0937 ; 1826
Keywords [en]
Clustering, Eccentricity Analysis, Federated Learning, HAR, Non-IID data, Computer aided instruction, Iterative methods, Learning systems, Pattern recognition, Privacy-preserving techniques, Centralised, Clusterings, Eccentricity analyse, Human activity recognition, IID data, ITS applications, Learning models, Well being, Health care
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-25227DOI: 10.1007/978-3-031-34204-2_41ISI: 001308331700041Scopus ID: 2-s2.0-85164039066ISBN: 9783031342035 (print)OAI: oai:DiVA.org:bth-25227DiVA, id: diva2:1785994
Conference
24th International Conference on Engineering Applications of Neural Networks, EANN 2023, León, 14 June through 17 June 2023
Part of project
HINTS - Human-Centered Intelligent Realities
Funder
Knowledge Foundation, 20220068Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2024-10-22Bibliographically approved
In thesis
1. Resource-Aware and Personalized Federated Learning via Clustering Analysis
Open this publication in new window or tab >>Resource-Aware and Personalized Federated Learning via Clustering Analysis
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Today’s advancement in Artificial Intelligence (AI) enables training Machine Learning (ML) models on the daily-produced data by connected edge devices. To make the most of the data stored on the device, conventional ML approaches require gathering all individual data sets and transferring them to a central location to train a common model. However, centralizing data incurs significant costs related to communication, network resource utilization, high volume of traffic, and privacy issues. To address the aforementioned challenges, Federated Learning (FL) is employed as a novel approach to train a shared model on decentralized edge devices while preserving privacy. Despite the significant potential of FL, it still requires considerable resources such as time, computational power, energy, and bandwidth availability. More importantly, the computational capabilities of the training devices may vary over time. Furthermore, the devices involved in the training process of FL may have distinct training datasets that differ in terms of their size and distribution. As a result of this, the convergence of the FL models may become unstable and slow. These differences can influence the FL process and ultimately lead to suboptimal model performance within a heterogeneous federated network.

In this thesis, we have tackled several of the aforementioned challenges. Initially, a FL algorithm is proposed that utilizes cluster analysis to address the problem of communication overhead. This issue poses a major bottleneck in FL, particularly for complex models, large-scale applications, and frequent updates. The next research conducted in this thesis involved extending the previous study to include wireless networks (WNs). In WSNs, achieving energy-efficient transmission is a significant challenge due to their limited resources. This has motivated us to continue with a comprehensive overview and classification of the latest advancements in context-aware edge-based AI models, with a specific emphasis on sensor networks. The review has also investigated the associated challenges and motivations for adopting AI techniques, along with an evaluation of current areas of research that need further investigation. To optimize the aggregation of the FL model and alleviate communication expenses, the initial study addressing communication overhead is extended to include a FL-based cluster optimization approach. Furthermore, to reduce the detrimental effect caused by data heterogeneity among edge devices on FL, a new study of group-personalized FL models has been conducted. Finally, taking inspiration from the previously mentioned FL models, techniques for assessing clients' contribution by monitoring and evaluating their behavior during training are proposed. In comparison with the most existing contribution evaluation solutions, the proposed techniques do not require significant computational resources.

The FL algorithms presented in this thesis are assessed on a range of real-world datasets. The extensive experiments demonstrated that the proposed FL techniques are effective and robust. These techniques improve communication efficiency, resource utilization, model convergence speed, and aggregation efficiency, and also reduce data heterogeneity when compared to other state-of-the-art methods.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2024. p. 260
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2024:04
Keywords
Federated Learning, Clustering Analysis, Eccentricity Analysis, Non- IID Data, Model Personalization
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-26081 (URN)978-91-7295-478-6 (ISBN)
Public defence
2024-05-17, C413A, Karlskrona, 10:00 (English)
Opponent
Supervisors
Available from: 2024-04-05 Created: 2024-04-05 Last updated: 2024-04-22Bibliographically approved

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Al-Saedi, Ahmed Abbas MohsinBoeva, Veselka

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