arXiv:2509.11819v2 Announce Type: replace Abstract: Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating with source clients while preserving data privacy. FDA faces two primary challenges: domain shifts between source and target data and limited labeled data at the target. Most existing FDA methods focus on domain shifts, assuming ample target data, yet often neglect the combined challenges of both domain shifts and data scarcity. Moreover, approaches that address both challenges fail to prioritize sharin
The proliferation of distributed data and increased privacy concerns are driving the need for more sophisticated federated learning approaches, especially in scenarios with domain shifts and data scarcity.
This development addresses key challenges in Federated Learning (FL), enabling more robust and privacy-preserving AI models in diverse, data-constrained environments, which is critical for sensitive applications.
Traditional federated learning models are augmented with advanced domain adaptation techniques that better handle both data privacy and performance in distributed, heterogeneous data settings.
- · Healthcare sector
- · Financial services
- · Privacy-focused AI companies
- · Edge computing platforms
- · Centralized data aggregation platforms
- · AI models reliant on large, uniform datasets
Improved performance and broader adoption of federated learning in sectors with strict data privacy regulations.
Reduced need for data sharing between organizations, fostering competitive advantages for those leveraging distributed AI.
Acceleration of sovereign AI initiatives as local data can be used to train powerful models without needing to leave national or organizational boundaries.
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