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 suggest that FI exhibits system-level behaviors that cannot be directly inherited from training-time federation or classical ensemble methods. Overall, this work provides a unifying perspective on FI and outlines open challenges that must be addressed to enable practical, scalable, and privacy-preserving collaborative inference systems.

翻译：联邦推理（FI）研究独立训练且私有拥有的模型如何在推理阶段进行协作，而无需共享数据或模型参数。尽管近期研究从不同视角探索了安全分布式推理，但对联邦推理的统一抽象与系统级理解仍然缺乏。本文提出将联邦推理定位为一种区别于联邦学习的协作范式，并识别出决定其可行性的两个基本要求：推理阶段的隐私保护与通过协作实现的实质性性能提升。我们将联邦推理形式化为一种受保护的协作计算，分析其核心设计维度，并考察当隐私约束、非独立同分布数据与有限可观测性在推理阶段共同作用时产生的结构性权衡。通过具体实例与实证分析，我们揭示了隐私保护推理、基于集成的协作以及激励对齐中反复出现的矛盾点。研究结果表明，联邦推理展现出无法直接从训练阶段联邦机制或经典集成方法继承的系统级行为。总体而言，本研究为联邦推理提供了统一的理论视角，并指出了构建实用、可扩展且隐私保护的协作推理系统必须解决的关键挑战。