Data-Centric Review of Federated Learning: How Data Properties Affect Model Convergence
Researchers published a comprehensive survey analyzing how data characteristics influence federated learning systems, which train machine learning models across multiple devices without centralizing data. The survey categorizes data challenges—particularly non-independent and identically distributed (non-IID) data—by their impact on convergence speed and stability. This work provides practitioners with actionable guidance for designing federated learning systems with predictable performance.
A new arXiv preprint presents the first survey to systematically examine federated learning through a data-centric lens, addressing a gap in existing literature that focuses on general foundations and security without deeply analyzing data's role. The authors analyze non-IID data heterogeneity—where different clients have different data distributions—into measurable traits and rank their influence on convergence as strong, medium, or light, with mechanisms explained across image, text, and graph domains. The survey also connects experimental data-splitting practices to real-world phenomena, exposing artifacts introduced by common protocols and their effects on accuracy. Additionally, it examines how data-related vulnerabilities and their defenses affect convergence under both clean and adversarial conditions, making the convergence-robustness trade-off explicit. The work synthesizes evidence across multiple data types and provides clear takeaways for each concern, serving as practical guidance for system designers.
What's missing
The abstract does not specify the number of papers reviewed, the publication timeline covered, or whether the survey includes comparisons to centralized learning baselines. Additionally, specific quantitative thresholds for what constitutes 'strong,' 'medium,' or 'light' influence on convergence are not detailed in the abstract.
What different sources said
- arXiv cs.AICenter
From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
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