Telefonaktiebolaget LM Ericsson (publ) (20250094571). GENERATIVE ADVERSARIAL-BASED ATTACK IN FEDERATED LEARNING
GENERATIVE ADVERSARIAL-BASED ATTACK IN FEDERATED LEARNING
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
Konstantinos Vandikas of Solna SE
GENERATIVE ADVERSARIAL-BASED ATTACK IN FEDERATED LEARNING
This abstract first appeared for US patent application 20250094571 titled 'GENERATIVE ADVERSARIAL-BASED ATTACK IN FEDERATED LEARNING
Original Abstract Submitted
a method performed by a client node is provided for generating a generative adversarial network (gan)-based attack for disruption of a global federated learning model. the method includes setting an attack strength factor to a value; and training the gan using the attack strength factor and an initial adversarial dataset to obtain a malicious weight matrix. the initial adversarial dataset is generated from or by initial weights matrix received from a network node of the global federated learning model and initial malicious weights derived from an initial attack on the global federated learning model that used a deterministic attack to obtain the malicious weight matrix. the method further includes generating the gan-based attack including an updated malicious weight matrix; and sending the updated malicious weight matrix to the network node.