The development and application of federated machine learning are facing the critical challenges about how to balance the tradeoff among privacy, security, performance, and efficiency, how to realize supervision covering the whole life cycle and how to get the explainable results. Then trustworthy federated machine learning is proposed to solve the above problem. In this standard, a general view on framework for trustworthy federated machine learning is provided in four parts: a principle in trustworthy federated machine learning, requirements from the perspective of different principles and different federated machine learning participants, and methods to realize trustworthy federated machine learning. It also provides some guidance on how trustworthy federated machine learning is used in various scenarios.
- Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active PAR
- PAR Approval
- 2022-06-16
Working Group Details
- Society
- IEEE Computer Society
Learn More About IEEE Computer Society - Sponsor Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
FT-FML - Framework for Trustworthy Federated Machine Learning
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Zuping Wu
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