Escaping saddle points in heterogeneous federated learning via distributed SGD with communication compression

• Main Authors: CHEN, Sijin, LI, Zhize, CHI, Yuejie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Federated learning; Machine learning; Communication compression; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/9493; https://ink.library.smu.edu.sg/context/sis_research/article/10493/viewcontent/1_s2.0_S2214140524001233_pvoa_cc_by_nc.pdf
• Institution: Singapore Management University

We consider the problem of finding second-order stationary points in the optimization of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of...