[Paper] A Distributed Framework for Privacy-Enhanced Vision Transformers on the Edge

Source: arXiv - 2512.09309v1

Overview

The paper introduces a distributed, hierarchical offloading framework that lets Vision Transformers (ViTs) run on edge devices while keeping visual data private. By slicing an image and sending the pieces to separate cloud servers—none of which ever sees the whole picture—the approach preserves privacy without sacrificing the accuracy of tasks like segmentation.

Key Contributions

• Privacy‑by‑design offloading: Guarantees that no single cloud server can reconstruct the original image.
• Hierarchical edge orchestration: A trusted edge device (phone, Jetson, etc.) partitions data, coordinates distribution, and performs the final aggregation locally.
• Adaptation to Vision Transformers: Demonstrates the framework on the Segment Anything Model (SAM), a state‑of‑the‑art ViT‑based segmentation tool.
• Near‑baseline performance: Shows that segmentation quality remains virtually unchanged compared with a monolithic cloud inference pipeline.
• Scalable architecture: Supports arbitrary numbers of cloud nodes, making it suitable for diverse edge‑cloud deployments.

Methodology

1. Edge‑side partitioning – The user’s device extracts the input image, splits it into N non‑overlapping patches, and encrypts each patch with lightweight symmetric keys.
2. Distributed inference – Each patch is sent to an independent cloud server that runs a partial ViT forward pass (e.g., early transformer layers). Because each server only sees a fragment, it cannot reconstruct the full scene.
3. Local aggregation – The edge device collects the intermediate feature maps, merges them according to the original spatial layout, and runs the remaining transformer layers plus the task‑specific head (e.g., SAM’s mask decoder).
4. Privacy guarantees – The system relies on two facts: (a) the edge device is trusted, and (b) the cloud servers are non‑colluding (they do not share data). The authors also discuss optional secret‑sharing or homomorphic encryption extensions for stronger guarantees.

The pipeline is implemented with standard deep‑learning libraries (PyTorch) and uses existing ViT checkpoints, so developers can plug it into their own models with minimal code changes.

Results & Findings

• Accuracy: The drop in segmentation quality is <0.5 %, which is within typical variance for ViT models.
• Latency: The extra network round‑trips add only a few milliseconds, well within interactive UI requirements.
• Privacy: Simulated adversarial reconstruction attacks failed to recover recognizable content from any single server’s view.

Overall, the framework delivers privacy gains comparable to full on‑device inference while keeping the computational load on the edge modest.

Practical Implications

• Edge‑first AI products: Mobile apps, AR glasses, and wearables can now offload heavy ViT workloads without exposing raw camera feeds, opening doors for privacy‑sensitive use cases (e.g., medical imaging, surveillance).
• Regulatory compliance: By ensuring that no third‑party server holds complete user data, the approach helps meet GDPR, CCPA, and emerging AI‑specific privacy regulations.
• Cost‑effective scaling: Companies can spin up cheap, stateless cloud workers for the early transformer layers, while the expensive decoder runs on the edge, reducing cloud compute bills.
• Developer friendliness: The framework is model‑agnostic; any ViT‑based architecture (classification, detection, segmentation) can be retrofitted with a few lines of code.
• Composable security: The design can be combined with other techniques—secure enclaves, differential privacy, or federated learning—to build multi‑layered privacy shields.

Limitations & Future Work

• Non‑collusion assumption: The privacy guarantee hinges on cloud servers not sharing data. The authors suggest cryptographic extensions (e.g., secret sharing) to relax this, but they add overhead.
• Edge resource constraints: While the final aggregation is lightweight, devices with extremely limited memory may still struggle with large patch counts.
• Network variability: The framework assumes relatively stable bandwidth; high latency or packet loss could degrade the interactive experience.
• Broader model support: Experiments focus on SAM; extending to other ViT families (e.g., Swin, DeiT) and to non‑vision tasks remains an open avenue.

Future research directions include formal privacy proofs, adaptive partitioning based on network conditions, and integration with hardware‑level trusted execution environments (TEE) for end‑to‑end security.

Authors

• Zihao Ding
• Mufeng Zhu
• Zhongze Tang
• Sheng Wei
• Yao Liu

Paper Information

• arXiv ID: 2512.09309v1
• Categories: cs.DC, cs.CR, cs.CV
• Published: December 10, 2025
• PDF: Download PDF