Data-Centric Review of Federated Learning: How Data Properties Affect Model Convergence
Researchers have published a comprehensive survey on arXiv examining how data-related challenges affect the convergence and stability of Federated Learning (FL) systems. FL allows multiple parties to collaboratively train machine learning models without sharing raw data, but inconsistencies in how data is distributed across clients create significant training instabilities. The survey is notable for being the first to systematically link concrete data properties, experimental splitting methods, and adversarial defenses to measurable convergence outcomes.
A new survey paper submitted to arXiv on June 9, 2026 provides a data-centric analysis of Federated Learning, a privacy-preserving machine learning paradigm in which multiple clients train a shared model without exposing their local datasets. The authors argue that prior FL surveys have addressed general foundations, security, and applications but have not systematically examined data itself as a primary source of vulnerability and instability. The paper makes three main contributions: it decomposes the non-IID (non-independent and identically distributed) data problem into measurable traits ranked by their influence on convergence as strong, medium, or light; it evaluates how common experimental data-splitting protocols relate to real-world phenomena and what artifacts they introduce; and it analyzes how data-related adversarial attacks and their defenses affect convergence speed and model accuracy. The survey covers evidence across image, text, and graph data modalities, and explicitly characterizes the trade-off between convergence speed and robustness under adversarial conditions. The authors position the work as actionable guidance for practitioners designing FL systems with predictable stability.
What's missing
As a preprint, this survey has not yet undergone formal peer review, so its claims about being the 'first' comprehensive data-centric FL survey and its rankings of non-IID trait influence on convergence have not been independently validated. The survey's scope and conclusions may evolve before formal publication.
What different sources said
- arXiv cs.AICenter
From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
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