Quasi-Global Momentum

Improved decentralized training with data heterogeneity

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge and may severely deteriorate the generalization performance. We propose a novel momentum-based method to mitigate this decentralized training difficulty.