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Work item:
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F.748.24 (ex F.TCEF-FML)
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Subject/title:
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Trusted contribution evaluation framework on federated machine learning services
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Status:
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Approved on 2024-04-15 [Issued from previous study period]
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Approval process:
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TAP
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Type of work item:
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Recommendation
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Version:
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New
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Equivalent number:
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-
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Timing:
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-
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Liaison:
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ITU-T SG13, SG20
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Supporting members:
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China Unicom; China Information Communication Technologies Group; ZTE Corporation; ETRI
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Summary:
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Federated machine learning (FML) is an emerging distributed framework that enables collaborative machine learning (ML) and model construction across distributed and decentralized datasets. FML service has distinctive features, such as where is the data where is the calculation, and data is available but not visible. It allows participants to jointly training ML models without sharing raw data, which can technically break data isolation and promote cooperation among the data owners.
FML service involves multiple participants who usually perform different contributions to ML model training tasks due to many impact factors of the participants. An effective and trusted contribution evaluation mechanism for FML service is essential to increase participation of the parties involved and can promote the sustainable development of FML services.
This Recommendation introduces a trusted contribution evaluation service on federated machine learning service which converges and takes advantage the technologies of FML and DLT, and provides relevant concept, characteristics, and requirements and use cases, and specifies relevant reference framework and common capabilities.
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Comment:
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-
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Reference(s):
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Historic references:
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Contact(s):
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| ITU-T A.5 justification(s): |
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First registration in the WP:
2021-05-21 11:58:01
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Last update:
2024-04-17 16:21:55
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