  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.
