This guide provides a reference framework for trustworthy Federated Machine Learning. The document provides guidance with respect to provable security for data and models, optimized model utility, controllable communication and computational complexity, explainable decision making and supervised processes. The guide describes three main aspects: 1) principles for trustworthy Federated Machine Learning, 2) requirements for different roles in trustworthy Federated Machine Learning, and 3) techniques to realize trustworthy Federated Machine Learning.
- 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
Learn More About 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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