The Evolution of AI Training Paradigms: From Centralized Control to a Technological Revolution of Decentralized Collaboration
In the full value chain of AI, model training is the most resource-intensive and technically challenging phase, directly determining the upper limit of the model's capabilities and its actual application effects. Compared to the lightweight calls in the inference stage, the training process requires continuous large-scale computing power investment, complex data processing workflows, and high-intensity optimization algorithm support, making it the true "heavy industry" of AI system construction. From an architectural paradigm perspective, training methods can be divided into four categories: centralized training, distributed training, federated learning, and the focus of this article, Decentralization training.
Centralized training is the most common traditional method, completed by a single entity within a local high-performance cluster, coordinating the entire training process, from hardware, underlying software, cluster scheduling systems, to all components of the training framework, all operated by a unified control system. This deeply collaborative architecture facilitates memory sharing and gradient synchronization.