Personalized Federated Learning for Statistical Heterogeneity
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929245427597312 |
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| author | Firdaus, Muhammad Rhee, Kyung-Hyune |
| author_facet | Firdaus, Muhammad Rhee, Kyung-Hyune |
| contents | The popularity of federated learning (FL) is on the rise, along with growing concerns about data privacy in artificial intelligence applications. FL facilitates collaborative multi-party model learning while simultaneously ensuring the preservation of data confidentiality. Nevertheless, the problem of statistical heterogeneity caused by the presence of diverse client data distributions gives rise to certain challenges, such as inadequate personalization and slow convergence. In order to address the above issues, this paper offers a brief summary of the current research progress in the field of personalized federated learning (PFL). It outlines the PFL concept, examines related techniques, and highlights current endeavors. Furthermore, this paper also discusses potential further research and obstacles associated with PFL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10254 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personalized Federated Learning for Statistical Heterogeneity Firdaus, Muhammad Rhee, Kyung-Hyune Machine Learning Networking and Internet Architecture The popularity of federated learning (FL) is on the rise, along with growing concerns about data privacy in artificial intelligence applications. FL facilitates collaborative multi-party model learning while simultaneously ensuring the preservation of data confidentiality. Nevertheless, the problem of statistical heterogeneity caused by the presence of diverse client data distributions gives rise to certain challenges, such as inadequate personalization and slow convergence. In order to address the above issues, this paper offers a brief summary of the current research progress in the field of personalized federated learning (PFL). It outlines the PFL concept, examines related techniques, and highlights current endeavors. Furthermore, this paper also discusses potential further research and obstacles associated with PFL. |
| title | Personalized Federated Learning for Statistical Heterogeneity |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2402.10254 |