Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
arXiv:2412.00452v3 Announce Type: replace Abstract: Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Worse still, the F-LN problem is exacerbated by the heterogeneity of FL, whereas clients experience different label-noise types, ratios, and data distribution. In this study, we first observe an intriguing phenomenon that the global model of FL exhibits a slow memorization of noisy labels, suggesting its ability to maintain reliable predictions and robust representatio
The proliferation of real-world federated learning applications highlights the critical need for robust models that can handle noisy, heterogeneous data distributions. This research directly addresses a significant practical bottleneck.
Improving Federated Learning's resilience to noisy labels expands its applicability across sensitive domains like healthcare and finance, accelerating the adoption of distributed AI systems. This enables more scalable and privacy-preserving AI development.
Federated Learning models can now maintain higher reliability and performance even when trained on disparate, imperfect datasets across various client devices. This lowers the barrier for deploying FL solutions in complex real-world environments.
- · Federated Learning platforms
- · Healthcare AI companies
- · Financial AI companies
- · AI developers
- · Centralized data labeling services (potentially, due to reduced reliance)
More widespread and effective deployment of AI models across distributed, privacy-sensitive data environments becomes feasible.
Reduced investment in manual data cleaning processes for federated datasets, shifting resources towards model explainability and robustness.
Accelerated development of privacy-preserving AI solutions becomes possible, potentially reshaping data governance regulations for AI.
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Read at arXiv cs.LG