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.

通过在分散式联邦学习中创建协作图，选择适合的合作方，从而解决数据异构性和通信限制带来的挑战。我们的方法通过一种新颖的、通信高效的策略来解决这些问题，提高资源效率。与传统方法不同，我们的方法通过考虑客户的组合关系在粒度级别上识别合作伙伴，增强个性化同时最小化通信开销。我们通过使用约束贪婪算法的双层优化框架实现这一目标，从而为个性化学习提供资源高效的协作图。通过在各种基准数据集上进行大规模评估，我们证明了我们的方法 DPFL 在处理实际数据异构性、最小化通信开销、提高资源效率以及在分散式联邦学习场景中构建个性化模型方面的优越性。