Contrastive learning in a federated environment

An apparatus configured to train a federated contrastive learning model, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• input a data point into a first neural network configured to output a feature of the data point;
• input the feature into a second neural network and a third neural network, wherein the second neural network is configured to output a predicted client identifier based on the feature, wherein the predicted client identifier is indicative of a predicted source of the data point, and wherein the third neural network is associated with a contrastive loss computation of the data point;
• and
• output:
• the contrastive loss computation, and
• a client identifier loss computation based on comparing the predicted client identifier to an actual client identifier, wherein the actual client identifier is indicative of an actual source of the data point.

An apparatus configured to train a federated contrastive learning model, comprising:

• means for inputting a data point into a first neural network configured to output a feature of the data point;
• means for inputting the feature into a second neural network and a third neural network, wherein the second neural network is configured to output a predicted client identifier based on the feature, wherein the predicted client identifier is indicative of a predicted source of the data point, and wherein the third neural network is associated with a contrastive loss computation of the data point;
• and
• means for outputting:
• the contrastive loss computation, and
• a client identifier loss computation based on comparing the predicted client identifier to an actual client identifier, wherein the actual client identifier is indicative of an actual source of the data point.