Generative adversarial-based attack in federated learning

A client node comprising a generative adversarial network (GAN) for generating a GAN-based attack for disruption of a global federated learning model, the client node comprising:

• at least one processor;
• at least one memory connected to the at least one processor and storing program code that is executed by the at least one processor to perform operations comprising:
• set an attack strength factor to a value;
• train the GAN using the attack strength factor and an initial adversarial dataset to obtain a malicious weight matrix, the initial adversarial dataset 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;
• generate the GAN-based attack comprising an updated malicious weight matrix;
• and
• send the updated malicious weight matrix to the network node.

A method performed by a network node for defending against a generative adversarial network (GAN)-based attack on a global federated learning model, the method comprising:

• receiving an updated weight matrix from a client node of the global federated learning model, the updated weight matrix generated by the GAN;
• passing the updated weight matrix through a weight statistics filter having a variable weight statistics threshold that adapts during training of the global federated learning model;
• and
• identifying the updated weight matrix as a benign update or a malicious update based on a value of the variable weight statistics threshold.

A network node for defending against a generative adversarial network (GAN)-based attack on a global federated learning model, the network node comprising:

• at least one processor;
• at least one memory connected to the at least one processor and storing program code that is executed by the at least one processor to perform operations comprising:
• receive an updated weight matrix from a client node of the global federated learning model, the updated weight matrix generated by the GAN;
• pass the updated weight matrix through a weight statistics filter having a variable weight statistics threshold that adapts during training of the global federated learning model;
• and
• identify the updated weight matrix as a benign update or a malicious update based on a value of the variable weight statistics threshold.