Architectural Blueprint For Heterogeneity-Resilient Federated Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916555204329472 |
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| author | Bashir, Satwat Dagiuklas, Tasos Kassai, Kasra Iqbal, Muddesar |
| author_facet | Bashir, Satwat Dagiuklas, Tasos Kassai, Kasra Iqbal, Muddesar |
| contents | This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Architectural Blueprint For Heterogeneity-Resilient Federated Learning Bashir, Satwat Dagiuklas, Tasos Kassai, Kasra Iqbal, Muddesar Machine Learning Distributed, Parallel, and Cluster Computing Networking and Internet Architecture This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies. |
| title | Architectural Blueprint For Heterogeneity-Resilient Federated Learning |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2403.04546 |