From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clients to train a shared-task model collaboratively without exposing their local data. While being a key component in any learning system, data is also a primary source of vulnerabilities and challenges, and a major determinant of a stable and well-converged training. Existing FL reviews describe general foundations, security practices, opportunities, challenges, and applications, without delving into diverse aspects of data and considering problems from the data perspective. They rarely provide a data-lens synthesis that links concrete data pro...
| Authors | Huong Nguyen, Mickaël Bettinelli, Amirhossein Ghaffari et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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