From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

The article presents a comprehensive survey on Federated Learning (FL), focusing on the impact of data heterogeneity on convergence and stability. It introduces three key advances: a ranking of non-IID data traits influencing convergence, a connection between experimental data splitting practices and their real-world implications, and an analysis of data-related vulnerabilities alongside their defenses affecting convergence under various conditions. This work provides actionable insights for practitioners, enabling better design of FL systems with predictable performance outcomes.