A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

Understand

Most federated learning (FL) methods use a client-server scheme, where clients communicate only with a central server.

• However, this scheme is prone to bandwidth bottlenecks at the server and has a single point of failure.
• In contrast, in a (fully) decentralized approach, clients communicate directly with each other, dispensing with the server and mitigating these issues.
• Yet, as the client network grows larger and sparser, the convergence of decentralized methods slows down, even failing to converge if the network is disconnected.