GWTF is the first practical, crash-tolerant decentralized framework for collaboratively training LLMs on heterogeneous volunteer clients. It handles node churn and unstable networks through a novel decentralized flow algorithm that optimizes microbatch routing. Evaluations on GPT- and LLaMa-like models show that GWTF reduces training time by up to 45% in challenging, geographically distributed settings.

Morph is a decentralized learning topology optimizer that adapts peer selection based on model dissimilarity to overcome non-IID data and static communication limits. By reshaping the graph through gossip-based discovery, it boosts robustness and performance. Experiments on CIFAR-10 and FEMNIST show Morph outperforming static and epidemic baselines, achieving higher accuracy, faster convergence, and more stable learning with fewer communication rounds.