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

2026-03-04 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Blockchain & Distributed Ledger Technology · Depth: Advanced, extended

Summary

Federated Inference (FI) is a collaborative paradigm enabling independently trained, privately owned models to jointly make predictions without sharing data or model parameters. This work formalizes FI as a protected collaborative computation, identifying inference-time privacy preservation and meaningful performance gains as core requirements. It introduces Federated Secure Ensemble Inference (FedSEI), a reference architecture that integrates Secure Multi-Party Computation (SMPC) for confidentiality and ensemble-based aggregation for utility. Empirical analysis with FedSEI reveals significant computational and communication overheads for SMPC-based inference, with latency increasing from milliseconds to minutes depending on model complexity, number of parties, and geographic distribution. The study also shows that ensemble effectiveness under non-IID data is highly context-dependent, and label-free reward allocation schemes for incentivizing participation struggle to align with ideal merit-based contributions, especially under severe data heterogeneity.

Key takeaway

For AI Scientists and Research Scientists developing collaborative AI systems, recognize that Federated Inference introduces unique system-level challenges distinct from Federated Learning. You should prioritize optimizing SMPC protocols and communication for geographically distributed deployments, as network latency often overwhelms local computation. Furthermore, be aware that designing fair, label-free incentive mechanisms for FI is a fundamental open problem, as simple proxies for contribution often fail under data heterogeneity, potentially leading to misaligned rewards.

Key insights

Federated Inference enables privacy-preserving, collaborative model predictions without sharing data or model parameters.

Principles

• Privacy preservation is a defining constraint, not an optional feature.
• Collaborative utility and system efficiency are key design axes.
• Incentive mechanisms are essential for sustained participation.

Method

FedSEI uses SMPC with additive secret sharing for privacy and ensemble aggregation for collaboration. Smart contracts handle incentive mechanisms and auditable completion, decoupling economic logic from the inference pipeline.

In practice

• SMPC inference incurs 50x-200x latency overhead vs. plain inference.
• Geographic network latency dominates end-to-end performance in wide-area deployments.
• Label-free reward schemes struggle to align with true model contribution.

Topics

• Federated Inference
• Privacy-Preserving AI
• Secure Multi-Party Computation
• Ensemble Learning
• Incentive Mechanisms

Code references

• thejungwon/FedSEI

Best for: AI Scientist, Research Scientist, AI Researcher, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.