This work tackles the challenges of data heterogeneity and communication limitations in decentralized federated learning. We focus on creating a collaboration graph that guides each client in selecting suitable collaborators for training personalized models that leverage their local data effectively. Our approach addresses these issues through a novel, communication-efficient strategy that enhances resource efficiency. Unlike traditional methods, our formulation identifies collaborators at a granular level by considering combinatorial relations of clients, enhancing personalization while minimizing communication overhead. We achieve this through a bi-level optimization framework that employs a constrained greedy algorithm, resulting in a resource-efficient collaboration graph for personalized learning. Extensive evaluation against various baselines across diverse datasets demonstrates the superiority of our method, named DPFL. DPFL consistently outperforms other approaches, showcasing its effectiveness in handling real-world data heterogeneity, minimizing communication overhead, enhancing resource efficiency, and building personalized models in decentralized federated learning scenarios.

翻译：本研究致力于解决去中心化联邦学习中数据异构性与通信受限的挑战。我们专注于构建一种协作图，以指导各客户端选择合适的协作方，从而训练能够有效利用其本地数据的个性化模型。我们的方法通过一种新颖且通信高效的策略应对这些问题，提升了资源利用效率。与传统方法不同，我们的方案通过考虑客户端的组合关系，在细粒度层面识别协作方，在增强个性化的同时最小化通信开销。我们通过一个采用约束贪婪算法的双层优化框架实现了这一目标，最终得到一个用于个性化学习的资源高效协作图。在多种数据集上与各类基线方法的广泛评估表明，我们命名为DPFL的方法具有优越性。DPFL始终优于其他方法，展现了其在处理现实世界数据异构性、最小化通信开销、提升资源效率以及在去中心化联邦学习场景中构建个性化模型方面的有效性。