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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2207.10308 |
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| _version_ | 1866917557024325632 |
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| author | Liu, Xiaoyuan Shi, Tianneng Xie, Chulin Li, Qinbin Hu, Kangping Kim, Haoyu Xu, Xiaojun Vu-Le, The-Anh Huang, Zhen Nourian, Arash Li, Bo Song, Dawn |
| author_facet | Liu, Xiaoyuan Shi, Tianneng Xie, Chulin Li, Qinbin Hu, Kangping Kim, Haoyu Xu, Xiaojun Vu-Le, The-Anh Huang, Zhen Nourian, Arash Li, Bo Song, Dawn |
| contents | Federated Learning (FL) has become a practical and widely adopted distributed learning paradigm. However, the lack of a comprehensive and standardized solution covering diverse use cases makes it challenging to use in practice. In addition, selecting an appropriate FL framework for a specific use case can be a daunting task. In this work, we present UniFed, the first unified platform for standardizing existing open-source FL frameworks. The platform streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks. In particular, to address the substantial variations in workflows and data formats, UniFed introduces a configuration-based schema-enforced task specification, offering 20 editable fields. UniFed also provides functionalities such as distributed execution management, logging, and data analysis.
With UniFed, we evaluate and compare 11 popular FL frameworks from the perspectives of functionality, privacy protection, and performance, through conducting developer surveys and code-level investigation. We collect 15 diverse FL scenario setups (e.g., horizontal and vertical settings) for FL framework evaluation. This comprehensive evaluation allows us to analyze both model and system performance, providing detailed comparisons and offering recommendations for framework selection. UniFed simplifies the process of selecting and utilizing the appropriate FL framework for specific use cases, while enabling standardized distributed experimentation and deployment. Our results and analysis based on experiments with up to 178 distributed nodes provide valuable system design and deployment insights, aiming to empower practitioners in their pursuit of effective FL solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_10308 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks Liu, Xiaoyuan Shi, Tianneng Xie, Chulin Li, Qinbin Hu, Kangping Kim, Haoyu Xu, Xiaojun Vu-Le, The-Anh Huang, Zhen Nourian, Arash Li, Bo Song, Dawn Machine Learning Federated Learning (FL) has become a practical and widely adopted distributed learning paradigm. However, the lack of a comprehensive and standardized solution covering diverse use cases makes it challenging to use in practice. In addition, selecting an appropriate FL framework for a specific use case can be a daunting task. In this work, we present UniFed, the first unified platform for standardizing existing open-source FL frameworks. The platform streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks. In particular, to address the substantial variations in workflows and data formats, UniFed introduces a configuration-based schema-enforced task specification, offering 20 editable fields. UniFed also provides functionalities such as distributed execution management, logging, and data analysis. With UniFed, we evaluate and compare 11 popular FL frameworks from the perspectives of functionality, privacy protection, and performance, through conducting developer surveys and code-level investigation. We collect 15 diverse FL scenario setups (e.g., horizontal and vertical settings) for FL framework evaluation. This comprehensive evaluation allows us to analyze both model and system performance, providing detailed comparisons and offering recommendations for framework selection. UniFed simplifies the process of selecting and utilizing the appropriate FL framework for specific use cases, while enabling standardized distributed experimentation and deployment. Our results and analysis based on experiments with up to 178 distributed nodes provide valuable system design and deployment insights, aiming to empower practitioners in their pursuit of effective FL solutions. |
| title | UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2207.10308 |