  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.
