[2603.02214] Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving

Computer Science > Artificial Intelligence arXiv:2603.02214 (cs) [Submitted on 9 Feb 2026] Title:Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving Authors:Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong, Jaeyeon Jang View a PDF of the paper titled Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving, by Jungwon Seo and 3 other authors View PDF HTML (experimental) Abstract:Federated Inference (FI) studies how independently trained and privately owned models can collaborate at inference time without sharing data or model parameters. While recent work has explored secure and distributed inference from disparate perspectives, a unified abstraction and system-level understanding of FI remain lacking. This paper positions FI as a distinct collaborative paradigm, complementary to federated learning, and identifies two fundamental requirements that govern its feasibility: inference-time privacy preservation and meaningful performance gains through collaboration. We formalize FI as a protected collaborative computation, analyze its core design dimensions, and examine the structural trade-offs that arise when privacy constraints, non-IID data, and limited observability are jointly imposed at inference time. Through a concrete instantiation and empirical analysis, we highlight recurring friction points in privacy-preserving inference, ensemble-based collaboration, and incentive alignment. Our findings sug...