mlsanityData to 7 Oct 2026

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

arXiv 2207.10308 ↗First posted 21 Jul 2022cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 33 of its 65 references that are in the map. First posted on arXiv 21 Jul 2022.

Builds on

33 of 65 references.

References · 33

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21 more
  1. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  2. 2019Fair Resource Allocation in Federated Learningcited by 389
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2023XFL: A High Performace, Lightweighted Federated Learning Frameworkcited by 2
  2. 2023MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflowscited by 0
  3. 2026Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deploymentcited by 0