Method to detect and defend against targetted adversarial attacks on a federated learning system

A system, the system comprising:

• one or more processors configured by machine-readable instructions to:
• transmit a first client model to a first computing device and a second client model to a second computing device,
• wherein, in a first training iteration, the first computing device trains the first client model and the second computing device trains the second client model;
• determine, based on the first training iteration, (i) a first predictive score ratio for the first computing device by comparing a first output of the first client model with a first output of a global model, and (ii) a first predictive score ratio for the second computing device by comparing a first output of the second client model with the first output of the global model;
• determine the first computing device and the second computing device match responsive to determining the first predictive score ratio for the first computing device and the first predictive score ratio for the second computing device are within a cluster threshold of each other,
• wherein, in a second training iteration subsequent to the first training iteration, the first computing device trains the first client model and the second computing device trains the second client model;
• determine, based on the second training iteration, (i) a second predictive score ratio for the first computing device by comparing a second output of the first client model with a second output of the global model, and (ii) a second predictive score ratio for the second computing device by comparing a second output of the second client model with the second output of the global model;
• and
• detect an anomaly in the first computing device responsive to (i) the determining the first computing device and the second computing device match, and (ii) determining the second predictive score ratio for the first computing device exceeds the second predictive score ratio for the second computing device by an amount above a difference threshold.

A method, comprising:

• transmitting, by a server, a client model to a computing device,
• wherein the computing device trains the client model using a first training data set in a first training iteration;
• receiving, by the server, labels for the first training data set from the computing device;
• determining, by the server, a first homogeneity score for the computing device based on a first frequency of a first class label for the first training data set compared to a frequency of one or more other class labels for the first training data set,
• wherein the computing device trains the client model using a second training data set in a second training iteration;
• determining, by the server based on the second training iteration, a second homogeneity score for the client model based on a second frequency of the first class label or a second class label for the second training data set for the computing device compared with a frequency of one or more other class labels for the second training data set for the computing device;
• and
• identifying, by the server, the computing device as an anomalous computing device based on the second homogeneity score exceeding the first homogeneity score for the computing device below a homogeneity threshold.