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.