arXiv:2502.17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks. Crucially, statistical heterogeneity in human-centric FL environments often results in an inequitable distribution of privacy risks, particularly affecting those whose sensitive attributes or behaviors make them outliers. To address this critical gap, we introduce FinP, a novel framework designed to formalize and e
As federated learning gains traction for privacy-preserving AI, the inherent disparities in privacy risk among users become more apparent and require dedicated solutions like FinP.
Ensuring fairness in privacy protection for AI systems, especially those dealing with sensitive data, is crucial for public trust, regulatory compliance, and ethical AI development.
The explicit formalization and addressing of privacy risk disparities in federated learning shifts the focus from general privacy to equitable privacy, pushing for more robust and fair data protection methodologies.
- · Vulnerable individuals in FL systems
- · AI ethics research and development
- · Organizations deploying sensitive FL models
- · Privacy-enhancing technologies (PETs)
- · AI systems with exploitable privacy disparities
- · Ad-hoc privacy solutions in FL
Increased development and adoption of frameworks like FinP to ensure equitable privacy in distributed AI.
New regulatory standards and best practices emerging that mandate fairness-in-privacy across various AI applications.
Enhanced public confidence in AI systems leading to broader acceptance and integration of AI in sensitive sectors.
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