arXiv:2606.02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets. However, gradient updates in FL retain structural patterns induced by non-independent and identically-distributed (non-IID) data, and these additional signals exposed by $\vare
The paper addresses a critical, ongoing challenge in privacy-preserving AI development, specifically the trade-offs between privacy, utility, and data heterogeneity in federated learning.
Improving privacy frameworks in federated learning is crucial for wider adoption of AI in sensitive domains like healthcare and finance, enabling better model performance without compromising data confidentiality.
This framework offers a method to enhance model utility in Heterogeneous Differential Privacy Federated Learning by addressing structural patterns from non-IID data, potentially leading to more effective and robust privacy-preserving AI systems.
- · Healthcare sector
- · Financial services
- · Privacy-focused AI companies
- · Distributed AI development
- · Organizations with weak privacy safeguards
- · AI models reliant on centralized, unprotected data
The adoption of such privacy-preserving techniques will increase trust in federated learning applications across sensitive industries.
Enhanced privacy in federated learning could accelerate the development of AI solutions that leverage distributed, proprietary datasets, fostering innovation.
This could lead to new regulatory frameworks and industry standards that mandate advanced privacy techniques for AI systems handling sensitive information.
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Read at arXiv cs.LG