Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data
This work addresses performance issues in federated learning systems for applications with heterogeneous data, representing an incremental improvement over existing momentum-based methods.
The paper tackled the suboptimal momentum initialization in federated learning with heterogeneous data by proposing Reversed Momentum Federated Learning (RMFL), which assigns exponentially decayed weights to gradients forward in time, and demonstrated its effectiveness on three benchmark datasets with varying heterogeneity levels.
Data Heterogeneity is a major challenge of Federated Learning performance. Recently, momentum based optimization techniques have beed proved to be effective in mitigating the heterogeneity issue. Along with the model updates, the momentum updates are transmitted to the server side and aggregated. Therefore, the local training initialized with a global momentum is guided by the global history of the gradients. However, we spot a problem in the traditional cumulation of the momentum which is suboptimal in the Federated Learning systems. The momentum used to weight less on the historical gradients and more on the recent gradients. This however, will engage more biased local gradients in the end of the local training. In this work, we propose a new way to calculate the estimated momentum used in local initialization. The proposed method is named as Reversed Momentum Federated Learning (RMFL). The key idea is to assign exponentially decayed weights to the gradients with the time going forward, which is on the contrary to the traditional momentum cumulation. The effectiveness of RMFL is evaluated on three popular benchmark datasets with different heterogeneity levels.