METHODS AND APPARATUS FOR ENHANCED SECURITY IN FEDERATED LEARNING MACHINE LEARNING OPERATIONS IN A COMMUNICATION NETWORK

Abstract: the disclosure pertains to methods and apparatus for federated learning (fl) artificial intelligence machine learning (aiml) implemented in a first security entity associated with a wireless transmit/receive unit (wtru), the method comprising: obtaining, by the first security entity, first information indicating security assessment data of the wtru in a federated learning network; determining, by the first security entity, based on the security assessment data, an indication of a security vulnerability of a trained fl aiml intermediate model; sending based on the determined indication of the security vulnerability, by the first security entity to a second security entity associated with a network node, second information indicating the trained fl aiml intermediate model; and sending, by the first security entity to the network node, third information associated with a training of the trained fl aiml.

Inventor(s): Zhibi Wang, Morteza Kheirkhah, Alec Brusilovsky, Ulises Olvera-Hernandez

CPC Classification: H04L63/1433 ({Vulnerability analysis})

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