Please use this identifier to cite or link to this item: https://knowledgecommons.lakeheadu.ca/handle/2453/5178
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dc.contributor.advisorFadlullah, Zubair-
dc.contributor.authorAs’ad, Anwar Munther-
dc.date.accessioned2023-06-21T15:25:50Z-
dc.date.available2023-06-21T15:25:50Z-
dc.date.created2023-
dc.date.issued2023-
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/5178-
dc.description.abstractFederated learning is a promising approach for training models on distributed data, driven by increasing demand in various industries. However, it faces several challenges, including communication bottlenecks and client data heterogeneity. Personalized asynchronous federated learning addresses these challenges by customizing the model for individual users based on their local data while trading model updates asynchronously. In this paper, we propose Personalized Moreau Envelopes-based Asynchronous Federated Learning (APFedMe) that combines personalized learning with asynchronous communication and Moreau Envelopes as clients’ regularized loss functions. Our approach uses the Moreau Envelopes to handle non-convex optimization problems and employs asynchronous updates to improve communication efficiency while mitigating heterogeneity data challenges through a personalized learning environment. We evaluate our approach on several datasets and compare it with PFedMe, FedAvg, and PFedAvg federated learning methods. Our experiments show that APFedMe outperforms other methods in terms of convergence speed and communication efficiency. Then, we mention some well-performing implementations to handle missing data in distributed learning. Overall, our work contributes to the development of more effective and efficient federated learning methods that can be applied in various real-world scenarios.en_US
dc.language.isoen_USen_US
dc.subjectFederated learningen_US
dc.subjectData heterogeneityen_US
dc.subjectMoreau envelopesen_US
dc.titleMoreau envelopes-based personalized asynchronous federated learning: improving practicality in distributed machine learningen_US
dc.typeThesisen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineComputer Scienceen_US
etd.degree.grantorLakehead Universityen_US
dc.contributor.committeememberAsaduzzaman, Muhammad-
dc.contributor.committeememberPathan, Al-Sakib Khan-
Appears in Collections:Electronic Theses and Dissertations from 2009

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