Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving

Researchers introduce Federated Inference (FI), a new collaborative paradigm where independently trained AI models can work together at inference time without sharing data or model parameters. The study identifies key requirements including privacy preservation and performance gains, while highlighting system-level challenges that differ from traditional federated learning approaches.

• →Federated Inference enables AI models to collaborate at inference time while maintaining data and parameter privacy.
• →The approach requires balancing privacy constraints with meaningful performance improvements through collaboration.
• →FI exhibits unique system-level behaviors that cannot be inherited from training-time federation or classical ensemble methods.
• →Key challenges include privacy-preserving inference, ensemble-based collaboration, and incentive alignment among participants.
• →The research provides a unified framework for understanding distributed inference systems with privacy constraints.