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Content for  TR 33.784  Word version:  19.0.0

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5  Key issuesp. 9

6  Solutionsp. 12

6.1  Solution #1: Security aspects on enhancements to LCS to support AIMLp. 12

6.2  Solution #2: LMF authorization mechanism in the AI/ML model retrieving scenariosp. 14

6.3  Solution #3: Solution for VFL member authorizationp. 14

6.4  Solution #4: Authorization of VFL member selectionp. 17

6.5  Solution #5: Authorization of VFL participants involving NWDAF and AFp. 21

6.6  Solution #6: Authorization mechanism through NRF and NEF for AF outside the PLMNp. 25

6.7  Solution #7: Authorization for selection of participant NWDAF instances for the 3rd party AF-initiated federated learningp. 26

6.8  Solution #8: Authorization for selection of participant AF for the NWDAF-initiated federated Learningp. 29

6.9  Solution #9: UE ID privacy protection of VFL between VFL membersp. 32

6.10  Solution #10: Privacy of sample alignmentp. 35

6.11  Solution #11: Protection of Privacy of VFL between VFL membersp. 37

6.12  Solution #12: VFL sample alignment initialled by NWDAFp. 39

6.13  Solution #13: Privacy protect mechanism for sample alignmentp. 40

6.14  Solution #14: Authorization for LCS Data Storage and Retrievalp. 41

6.15  Solution #15: Reuse the existing SBA mechanisms for protection of communication data in VFL training process.p. 43

6.16  Solution #16: LMF authorization for AI/ML model retrieval from NWDAF containing MTLFp. 44

6.17  Solution #17: Privacy of VFL between VFL membersp. 45

7  Conclusionsp. 46

$  Change historyp. 48


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