Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration

This paper proposes DFedCata, an algorithm that improves decentralized federated learning (a distributed AI training method where multiple devices train models together without a central server). The main problem addressed is that when training data differs across devices (called data heterogeneity), the models converge slowly and perform poorly. DFedCata uses two mathematical techniques, the Moreau envelope function and Nesterov's extrapolation step, to handle these inconsistencies and speed up training.