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Distributed learning systems have enabled training large-scale models overlarge amount of data in significantly shorter time. In this paper, we focus ondecentralized distributed deep learning systems and aim to achieve differentialprivacy with good convergence rate and low communication cost. To achieve thisgoal, we propose a new learning algorithm LEASGD (Leader-Follower ElasticAveraging Stochastic Gradient Descent), which is driven by a novelLeader-Follower topology and a differential privacy model.We provide atheoretical analysis of the convergence rate and the trade-off between theperformance and privacy in the private setting.The experimental results showthat LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGDby achieving steadily lower loss within the same iterations and by reducing thecommunication cost by 30%. In addition, LEASGD spends less differential privacybudget and has higher final accuracy result than DPSGD under private setting.

LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning