Distributed learning preserving model security

Abstract Distributed machine learning employs a central fusion server that coordinates the distributed learning process. Preferably, each of set of learning agents that are typically distributed from one another initially obtains initial parameters for a model from the fusion server. Each agent trains using a dataset local to the agent. The parameters that result from this local training (for a current iteration) are then passed back to the fusion server in a secure manner, and a partial homomorphic encryption scheme is then applied. In particular, the fusion server fuses the parameters from all the agents, and it then shares the results with the agents for a next iteration. In this approach, the model parameters are secured using the encryption scheme, thereby protecting the privacy of the training data, even from the fusion server itself.
  • Dinesh Verma (IBM US)
  • Supriyo Chakraborty (IBM US)
  • Changchang Liu (IBM US)
Date Apr-2020
Venue U.S. Patent Application 16/164,846, filed October 19, 2018 [link]