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Federated Learning (FL) has become a practical and widely adopted distributedlearning paradigm. However, the lack of a comprehensive and standardizedsolution covering diverse use cases makes it challenging to use in practice. Inaddition, selecting an appropriate FL framework for a specific use case can bea daunting task. In this work, we present UniFed, the first unified platformfor standardizing existing open-source FL frameworks. The platform streamlinesthe end-to-end workflow for distributed experimentation and deployment,encompassing 11 popular open-source FL frameworks. In particular, to addressthe substantial variations in workflows and data formats, UniFed introduces aconfiguration-based schema-enforced task specification, offering 20 editablefields. UniFed also provides functionalities such as distributed executionmanagement, logging, and data analysis.  With UniFed, we evaluate and compare 11 popular FL frameworks from theperspectives of functionality, privacy protection, and performance, throughconducting developer surveys and code-level investigation. We collect 15diverse FL scenario setups (e.g., horizontal and vertical settings) for FLframework evaluation. This comprehensive evaluation allows us to analyze bothmodel and system performance, providing detailed comparisons and offeringrecommendations for framework selection. UniFed simplifies the process ofselecting and utilizing the appropriate FL framework for specific use cases,while enabling standardized distributed experimentation and deployment. Ourresults and analysis based on experiments with up to 178 distributed nodesprovide valuable system design and deployment insights, aiming to empowerpractitioners in their pursuit of effective FL solutions.

UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks