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Contribution Prediction in Federated Learning via Client Behavior Evaluation
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. (AIDA)ORCID iD: 0000-0001-6061-0861
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-3118-5058
2025 (English)In: Future Generation Computer Systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 166, article id 107639Article in journal (Refereed) Published
Abstract [en]

Federated learning (FL), a decentralized machine learning framework that allows edge devices (i.e., clients) to train a global model while preserving data/client privacy, has become increasingly popular recently. In FL, a shared global model is built by aggregating the updated parameters in a distributed manner. To incentivize data owners to participate in FL, it is essential for service providers to fairly evaluate the contribution of each data owner to the shared model during the learning process. To the best of our knowledge, most existing solutions are resource-demanding and usually run as an additional evaluation procedure. The latter produces an expensive computational cost for large data owners. In this paper, we present simple and effective FL solutions that show how the clients’ behavior can be evaluated during the training process with respect to reliability, and this is demonstrated for two existing FL models, Cluster Analysis-based Federated Learning (CA-FL) and Group-Personalized FL (GP-FL), respectively. In the former model, CA-FL, the frequency of each client to be selected as a cluster representative and in that way to be involved in the building of the shared model is assessed. This can eventually be considered as a measure of the respective client data reliability. In the latter model, GP-FL, we calculate how many times each client changes a cluster it belongs to during FL training, which can be interpreted as a measure of the client's unstable behavior, i.e., it can be considered as not very reliable. We validate our FL approaches on three LEAF datasets and benchmark their performance to two baseline contribution evaluation approaches. The experimental results demonstrate that by applying the two FL models we are able to get robust evaluations of clients’ behavior during the training process. These evaluations can be used for further studying, comparing, understanding, and eventually predicting clients’ contributions to the shared global model.

Place, publisher, year, edition, pages
Elsevier, 2025. Vol. 166, article id 107639
Keywords [en]
Behavior monitoring; Clustering analysis, Contribution evaluation, Eccentricity analysis, Federated learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-26080DOI: 10.1016/j.future.2024.107639ISI: 001407806400001Scopus ID: 2-s2.0-85211047272OAI: oai:DiVA.org:bth-26080DiVA, id: diva2:1849017
Part of project
HINTS - Human-Centered Intelligent RealitiesSERT- Software Engineering ReThought, Knowledge Foundation
Funder
Knowledge Foundation, 20220068Knowledge Foundation, 20180010Available from: 2024-04-05 Created: 2024-04-05 Last updated: 2025-02-11Bibliographically 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, VeselkaCasalicchio, Emiliano

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