FONN: Federated Optimization with Nys-Newton
C Nagaraju, Mrinmay Sen, Chalavadi Krishna Mohan · 2023
Federated optimization or federated learning (FL) involves optimization of the global model or the server model by minimizing the global loss function which is weighted average of all the local loss functions. The optimization of the global model requires faster convergence to reduce the number of communication rounds or global iterations which is one of the major challenge in federated optimization. This paper propose FONN which handles this communication overhead in federated optimization by utilizing Nys-Newton, while updating local models. As compared to existing state-of-the-art FL algorithms, SCAFFOLD, GIANT and DONE, utilization of Nys-Newton leads to better convergence and reduction in communication rounds or global iterations while achieving a desired performance from the global model which may be observed from the experimental results on various heterogeneously partitioned datasets.