Systems and methods for federated learning

A system for federated learning including validation of training data on client devices before training models based on the training data, the system comprising:

• a client device, one or more first processors, and a first non-transitory, computer-readable medium storing first instructions that, when executed by the one or more first processors, cause first operations comprising:
• accessing local data to be used for training a local model on the client device;
• generating, based on first validation data received from a first external server, event metadata corresponding to events in the local data, wherein the first validation data is related to the events in the local data, and wherein the first validation data is inaccessible to a central server;
• based on comparing the local data and the event metadata, determining to exclude a portion of the local data that is not validated by the event metadata, and generating validated local data;
• training the local model based on the validated local data;
• processing, using a data de-identification function, the event metadata to generate de-identified event metadata;
• transmitting the local model and the de-identified event metadata to the central server;
• and
• the central server comprising one or more second processors and a second non-transitory, computer-readable medium storing second instructions that, when executed by the one or more second processors, cause second operations comprising:
• receiving, from the client device, the local model and the de-identified event metadata;
• generating, based on second validation data received from a second external server, corroborating metadata corresponding to the events in the de-identified event metadata, wherein the second validation data is related to the events in the de-identified event metadata, and wherein the second validation data is inaccessible to the client device;
• based on comparing the de-identified event metadata and the corroborating metadata, determining whether to exclude the local model from updates to a global model;
• and
• based on determining to not exclude the local model, updating the global model based on the local model.