Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
arXiv:2606.00986v1 Announce Type: new Abstract: Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains such as healthcare and institutional data sharing. FL keeps data local to clients while communicating only model updates, such as gradients or model deltas. Nevertheless, these updates can expose private client data through gradient inversion attacks (GIAs). We study this risk for tabular FL under an honest-but-curious server threat model across FL protocols, client bat
The proliferation of federated learning in privacy-sensitive sectors necessitates immediate solutions to known vulnerabilities like gradient inversion attacks, as regulatory and ethical pressures increase.
This research highlights critical security gaps in current federated learning implementations and proposes methodologies for more robust privacy preservation, which is vital for trust and adoption in sensitive domains.
Understanding of the specific vulnerabilities of tabular FL to gradient inversion attacks is advanced, enabling the development of more targeted and effective privacy-preserving mechanisms.
- · Healthcare sector
- · Financial services
- · AI ethics and privacy researchers
- · Federated learning platform providers
- · Untrustworthy AI solutions
- · Organizations with inadequate privacy protocols
Increased adoption of privacy-preserving techniques in federated learning for sensitive data applications.
Development of industry standards and certifications for secure federated learning, driving competitive advantage.
A potential shift in regulatory frameworks demanding higher privacy guarantees for collaborative AI models across international borders.
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Read at arXiv cs.LG