Utilizing elastic weight consolidation (ewc) loss term(s) to mitigate catastrophic forgetting in training machine learning model(s)

Abstract

Processor(s) of a client device can receive global weights of a global ML model from a remote system, obtain a client device data set, determine a Fisher information matrix for the client data set, and transmit the Fisher information matrix for the client data set to the remote system. Further, processor(s) of the remote system can determine a corresponding elastic weight consolidation (EWC) loss term for each of the global weights based on at least the Fisher information matrix, generate a server update for the global ML model based on (i) processing server data remotely at the remote system and using the global ML model and (ii) based on the corresponding EWC loss term for each of the global weights, and update the global weights of the global ML model based on the server update.