从退化到加速：联邦优化中消除步间不同步的一种校正方法
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization
摘要：
在联邦优化的设置中，全局模型被定期聚合，当参与者通过有效地利用他们的计算资源进行模型训练时，会发生步进式优化。众所周知，在非独立同定原则下，逐步修正主义会导致客观不一致。数据，这降低了模型的准确性。为了解决这个问题，我们提出了一个新的算法FedaGrac，它校准的局部方向的预测的全球方向。利用估计的方向，我们保证聚合模型不会过度偏离全局最优，同时充分利用更快节点的局部更新。我们从理论上证明了FedaGrac比现有的方法具有更高的收敛速度阶数，并且消除了步长不确定性的负面影响。实验结果表明，该算法加快了训练速度，提高了最终的准确率。
英文摘要：
In the setting of federated optimization, where a global model is aggregated periodically, step asynchronism occurs when participants conduct model training by efficiently utilizing their computational resources. It is well acknowledged that step asynchronism leads to objective inconsistency under non-i.i.d. data, which degrades the model's accuracy. To address this issue, we propose a new algorithm FedaGrac, which calibrates the local direction to a predictive global orientation. Taking advantage of the estimated orientation, we guarantee that the aggregated model does not excessively deviate from the global optimum while fully utilizing the local updates of faster nodes. We theoretically prove that FedaGrac holds an improved order of convergence rate than the state-of-the-art approaches and eliminates the negative effect of step asynchronism. Empirical results show that our algorithm accelerates the training and enhances the final accuracy.
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