Decentralized learning (DL) enables a set of nodes to train a model collaboratively without central coordination, offering benefits for privacy and scalability. However, DL struggles to train a high accuracy model when the data distribution is non-independent and identically distributed (non-IID) and when the communication topology is static. To address these issues, we propose Morph, a topology optimization algorithm for DL. In Morph, nodes adaptively choose peers for model exchange based on maximum model dissimilarity. Morph maintains a fixed in-degree while dynamically reshaping the communication graph through gossip-based peer discovery and diversity-driven neighbor selection, thereby improving robustness to data heterogeneity. Experiments on CIFAR-10 and FEMNIST with up to 100 nodes show that Morph consistently outperforms static and epidemic baselines, while closely tracking the fully connected upper bound. On CIFAR-10, Morph achieves a relative improvement of 1.12x in test accuracy compared to the state-of-the-art baselines. On FEMNIST, Morph achieves an accuracy that is 1.08x higher than Epidemic Learning. Similar trends hold for 50 node deployments, where Morph narrows the gap to the fully connected upper bound within 0.5 percentage points on CIFAR-10. These results demonstrate that Morph achieves higher final accuracy, faster convergence, and more stable learning as quantified by lower inter-node variance, while requiring fewer communication rounds than baselines and no global knowledge.

翻译：去中心化学习使得一组节点能够在无需中央协调的情况下协同训练模型，在隐私保护和可扩展性方面具有优势。然而，当数据分布呈现非独立同分布特性且通信拓扑结构保持静态时，去中心化学习难以训练出高精度模型。为解决这些问题，我们提出了Morph——一种面向去中心化学习的拓扑优化算法。在Morph中，节点基于最大模型差异度自适应地选择模型交换对等节点。该算法通过基于随机通信的对等节点发现机制和多样性驱动的邻居选择策略，在维持固定入度的同时动态重塑通信图，从而提升对数据异质性的鲁棒性。在包含多达100个节点的CIFAR-10和FEMNIST数据集上的实验表明，Morph始终优于静态拓扑和随机传播基线方法，同时紧密逼近全连接拓扑的理论上界。在CIFAR-10数据集上，相较于最先进的基线方法，Morph在测试准确率上实现了1.12倍的相对提升。在FEMNIST数据集上，Morph的准确率比随机传播学习高出1.08倍。在50个节点的部署场景中，Morph在CIFAR-10数据集上将与全连接理论上界的差距缩小至0.5个百分点以内。这些结果表明，Morph在实现更高最终准确率、更快收敛速度的同时，通过更低的节点间方差量化出更稳定的学习过程，且相较于基线方法需要更少的通信轮次且无需全局知识。