Privacy Preserving Decentralized Learning With Positive-Incentive Noise

Summary

Researchers introduce PP-DPIN, a privacy preserving stochastic algorithm for decentralized learning, built on a new mechanism called Positive-Incentive Noise Generator (PING). PING uses network topologies and lightweight encryption-decryption operations to generate correlated noise, aiming to remove the negative effect of privacy noise on convergence while defending against colluding inference attacks. The authors report that at least half of the nodes achieve arbitrarily strong privacy guarantees, and that experiments on computer vision tasks show better performance and attack robustness than state-of-the-art methods.

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