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

2026-02-09

AI Summary

This work proposes Federated Inference (FI) as a complementary paradigm to federated learning, enabling secure collaboration among private models during inference without sharing data or model parameters. The study introduces the first unified abstraction framework for FI, articulating two core objectives: preserving privacy during inference and achieving performance gains through collaboration. Building upon secure multi-party computation, the authors design a privacy-preserving collaborative inference architecture that integrates ensemble learning and incentive mechanisms. Systematic modeling and empirical analysis are conducted under non-IID data distributions and stringent privacy constraints. Experimental results reveal critical trade-offs among privacy, collaboration efficacy, and incentive alignment, underscoring the necessity of designing FI systems independently from conventional training-centric paradigms.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Game Theory and Economic Paradigms: Coordination and Collaboration

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Privacy-enhancing technologies
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

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 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.

Problem

• Federated Inference
• privacy preservation
• collaborative inference
• model serving
• incentive alignment

Innovation

• Federated Inference
• Privacy-Preserving Inference
• Collaborative Model Serving
• Incentive Alignment
• Non-IID Data