Systems and methods for utilizing federated machine-learning to protect against potentially malicious data

Abstract

The disclosed computer-implemented method for utilizing federated machine-learning to protect against potentially malicious data may include (i) arranging a set of client devices into groups for applying a federated machine-learning model, (ii) determining model updates for each of the groups over a predetermined period, (iii) training one or more recurrent neural networks to derive a low-dimensional representation of the model updates, (iv) calculating a data quality score for each of the client devices based on the model updates, (v) applying the federated machine-learning model to classify data instances on each of the client devices as including clean data or potentially corrupt data, and (vi) performing a security action that protects against the potentially malicious data by tagging the data instances classified as the potentially corrupt data. Various other methods, systems, and computer-readable media are also disclosed.