Accelerating Federated Learning via Momentum Gradient Descent

• Published in: IEEE transactions on parallel and distributed systems Vol. 31; no. 8; pp. 1754 - 1766
• Main Authors: Liu, Wei, Chen, Li, Chen, Yunfei, Zhang, Wenyi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerating convergence; Acceleration; Computational modeling; Computer simulation; Convergence; Data models; Distributed databases; distributed machine learning; Federated learning; Iterative methods; Machine learning; Momentum; momentum gradient descent; Servers; Upper bounds

Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize first-order gradient descent (GD) and do not consider the preceding iterations...