Integrating Graph Neural Networks with Federated Learning

The integration of GNNs with FL has emerged as a compelling paradigm for building resilient, secure, and adaptive distributed learning systems. While FL addresses privacy and scalability challenges by enabling decentralized model training without direct data sharing, it struggles with issues, such as non-IID data distributions, system heterogeneity, and vulnerability to adversarial attacks. GNNs, on the other hand, are well-suited to capture relational structures and higher-order dependencies [21], making them a natural complement to FL in contexts where clients, data, and updates exhibit graph-like interactions [9]. By embedding FL into graph-structured frameworks, researchers can leverage structural insights to improve robustness against poisoning and Byzantine attacks, enhance personalization for heterogeneous clients, and optimize communication efficiency in large-scale, dynamic environments [11]. This chapter explores the convergence of GNNs and FL, examining how graph-based techniques can enrich FL with new capabilities for resilience, adaptability, and trustworthiness across diverse application domains, such as IoT, healthcare, autonomous systems, and the Metaverse.