OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
arXiv:2605.20276v1 Announce Type: new Abstract: The global deployment of edge intelligence operates across heterogeneous legal frameworks. While some regions permit centralized learning (CL) via cloud data aggregation, others enforce strict data localization, necessitating federated learning (FL). This operational dichotomy introduces two incompatible optimization regimes (i.e., unbiased global gradients yet coupled with internal covariate shift in CL versus biased, drift-prone local updates in FL), resulting in that any naive integration of the two lacks rigorous theoretical guarantees. To fi
The increasing divergence in global data governance, particularly between regions permitting centralized data aggregation and those enforcing strict data localization, necessitates novel technical solutions for AI deployment.
This research addresses a critical challenge for global AI adoption by proposing a unified framework that can reconcile disparate legal and regulatory environments, ensuring robust and theoretically sound AI operations.
The proposed 'OmniISR' framework offers a theoretical guarantee for integrating centralized and federated learning, potentially enabling more versatile and compliant AI deployments across diverse legal landscapes without sacrificing performance or stability.
- · Multinational corporations
- · AI service providers
- · Regions with strict data localization laws
- · Academic researchers in distributed AI
- · AI models reliant solely on centralized data
- · Companies unable to adapt to hybrid learning paradigms
Wider and more compliant deployment of AI at the edge in industries with sensitive data.
Increased competition among AI-driven services as deployment becomes less geographically constrained by data regulations.
New standards and best practices for global AI development and deployment emerge, influencing international regulatory harmonization efforts.
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Read at arXiv cs.LG