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.

翻译：摘要：联邦学习（FL）已成为一种实用且被广泛采纳的分布式学习范式。然而，由于缺乏覆盖多种应用场景的全面标准化解决方案，其实际应用仍面临挑战。此外，针对特定用例选择合适的FL框架亦是一项艰巨任务。本文提出UniFed——首个用于标准化现有开源FL框架的统一平台。该平台涵盖了11种主流开源FL框架，可简化分布式实验与部署的端到端工作流程。特别地，为应对工作流与数据格式的显著差异，UniFed引入了一种基于配置、模式强制的任务规范，提供20个可编辑字段。平台还提供分布式执行管理、日志记录及数据分析等功能。借助UniFed，我们通过开发者调查与代码级分析，从功能性、隐私保护及性能三个维度对11种主流FL框架进行了评估与比较。我们采集了15种不同的FL场景配置（例如横向与纵向设置）用于框架评价。这一综合评价使我们能够分析模型与系统性能，提供详细对比并为框架选择提出建议。UniFed简化了针对特定用例选择与使用合适FL框架的过程，同时实现了标准化的分布式实验与部署。基于最多178个分布式节点的实验所得结果与分析，我们提供了有价值的系统设计与部署见解，旨在助力从业者探索高效的FL解决方案。