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Work item:
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M.3387 (ex M.rfmls)
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Subject/title:
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Management requirements for federated machine learning systems
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Status:
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Approved on 2024-03-11 [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 SG20, IEEE
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Supporting members:
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-
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Summary:
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Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements. Many routine operations in big data systems and applications, such as merging user data from various sources to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning (FML) is to provide a viable solution that empowers machine learning applications to utilize data in a distributed manner. In an FML framework, the data owners do not exchange raw data directly and do not allow any party to infer the private information of other parties. In order to facilitatepromote the construction and use of federated machine learning models (FMLMs) and improve the quality of FML service, this draft Recommendation specifies the management requirements for federated machine learning systems (FMLSs), including the functional architecture of FMLSs, as well as the requirements of the basic management domain, model management domain, and data management domain. This draft Recommendation is applicable to the architecture design, research, and development of FMLSs.
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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:
2021-06-15 16:40:59
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Last update:
2024-05-01 14:46:55
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