HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation

Researchers propose HO-SFL (Hybrid-Order Split Federated Learning), a new framework that enables memory-efficient fine-tuning of large AI models on edge devices by eliminating backpropagation on client devices while maintaining convergence speed comparable to traditional methods. The approach significantly reduces communication costs and memory requirements for distributed AI training.