<ul><li>In the paradigm of decentralized learning, a group of agents collaborates to learn a global model using distributed datasets without a central server.</li><li>The heterogeneity of the local data across agents makes learning a robust global model challenging.</li><li>PDSL is a privacy-preserved decentralized stochastic learning algorithm that addresses these challenges using Shapley values to measure neighbor contributions and differential privacy to prevent privacy leakage.</li><li>The PDSL algorithm demonstrates efficacy in privacy preservation and convergence, supported by theoretical analysis and extensive experiments.</li></ul>

PDSL: Privacy-Preserved Decentralized Stochastic Learning with Heterogeneous Data Distribution

Discover more