The broad application of artificial intelligence techniques ranging from
self-driving vehicles to advanced medical diagnostics afford many benefits.
Federated learning is a new breed of artificial intelligence, offering
techniques to help bridge the gap between personal data protection and
utilization for research and commercial deployment, especially in the use-cases
where security and privacy are the key concerns. Here, we present OpenFed, an
open-source software framework to simultaneously address the demands for data
protection and utilization. In practice, OpenFed enables state-of-the-art model
development in low-trust environments despite limited local data availability,
which lays the groundwork for sustainable collaborative model development and
commercial deployment by alleviating concerns of asset protection. In addition,
OpenFed also provides an end-to-end toolkit to facilitate federated learning
algorithm development as well as several benchmarks to fair performance
comparison under diverse computing paradigms and configurations.

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