A new distributed SGD algorithm integrates Paillier homomorphic encryption with heterogeneous random stepsizes and an attenuation factor to deliver privacy against honest-but-curious agents and eavesdroppers while converging almost surely to the optimum.
Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization , year=
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Privacy-Preserving Distributed Stochastic Optimization with Homomorphic Encryption and Heterogeneous Stepsizes
A new distributed SGD algorithm integrates Paillier homomorphic encryption with heterogeneous random stepsizes and an attenuation factor to deliver privacy against honest-but-curious agents and eavesdroppers while converging almost surely to the optimum.