US 12,353,604 B2
Detecting client isolation attacks in federated learning through overfitting monitoring
Maira Beatriz Hernandez Moran, Rio de Janeiro (BR); Paulo Abelha Ferreira, Rio de Janeiro (BR); and Pablo Nascimento da Silva, Niterói (BR)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Oct. 4, 2022, as Appl. No. 17/937,842.
Prior Publication US 2024/0111903 A1, Apr. 4, 2024
Int. Cl. G06F 21/64 (2013.01); G06N 20/00 (2019.01)
CPC G06F 21/64 (2013.01) [G06N 20/00 (2019.01)] 16 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving at a client node of a federation a global machine-learning model that is to be trained by the client node using a training dataset that is local to the client node;
in response to receiving the global machine-learning model, determining at the client node if the global machine-learning model is trending toward an overfitted state using a validation dataset, the validation dataset being a subset of data local to the client node that is not included in the training dataset, the overfitted state being indicative that the global machine-learning model has not been received from a server that is part of the federation because of a client isolation attack on the client node, wherein determining if the global machine-learning model is trending toward an overfitted state using the validation dataset comprises:
determining a training error when the global machine-learning model is trained using the training dataset;
determining a validation error when the global machine-learning model is trained using the validation dataset;
comparing the training error to a training error threshold; and
comparing the validation error to a validation error threshold, and it is indicative that the global machine-learning model is in the overfitted state when the training error is above the training error threshold and the validation error is below the validation error threshold;
in response to determining that the global machine-learning model is trending towards the overfitting state, causing the client node to leave the federation; and
in response to determining that the global machine-learning model is not trending towards the overfitted state, training the global machine-learning model using the training dataset to thereby update the global machine-learning model.