在联邦物联网中自动修复模型权重畸变，兼顾隐私与稳定性
DP-EMAR: A Differentially Private Framework for Autonomous Model Weight Repair in Federated IoT Systems
- 基于误差模型检测传输畸变，先修复再加差分隐私噪声
- 在通信干扰下仍保持接近基线的性能，收敛稳定
- 适合资源受限、易受攻击的物联网联邦学习场景
联邦学习（FL）可在不共享原始数据的情况下实现分布式训练，但在资源受限的物联网网络中，模型权重畸变仍是主要挑战。在多层级联邦物联网（Fed-IoT）系统中，不稳定的连接和恶意干扰会悄然改变传输参数，导致收敛性下降。我们提出DP-EMAR，一种基于差分隐私的误差建模自主修复框架，能在联邦聚合过程中检测并重建传输引起的畸变。该框架先估计污染模式并进行自适应修正，再添加隐私噪声，实现在不破坏保密性的前提下完成网络内修复。通过将差分隐私（DP）与安全聚合（SA）结合，框架可区分DP噪声与真实传输错误。在异构物联网传感器与图数据集上的实验表明，DP-EMAR在通信畸变条件下保持收敛稳定性，性能接近基线，并满足严格的(ε, δ)-差分隐私保证。该框架提升了联邦物联网学习的鲁棒性、通信效率与可信度。
原文摘要 · Abstract (English)
Federated Learning (FL) enables decentralized model training without sharing raw data, but model weight distortion remains a major challenge in resource constrained IoT networks. In multi tier Federated IoT (Fed-IoT) systems, unstable connectivity and adversarial interference can silently alter transmitted parameters, degrading convergence. We propose DP-EMAR, a differentially private, error model based autonomous repair framework that detects and reconstructs transmission induced distortions during FL aggregation. DP-EMAR estimates corruption patterns and applies adaptive correction before privacy noise is added, enabling reliable in network repair without violating confidentiality. By integrating Differential Privacy (DP) with Secure Aggregation (SA), the framework distinguishes DP noise from genuine transmission errors. Experiments on heterogeneous IoT sensor and graph datasets show that DP-EMAR preserves convergence stability and maintains near baseline performance under communication corruption while ensuring strict (epsilon, delta)-DP guarantees. The framework enhances robustness, communication efficiency, and trust in privacy preserving Federated IoT learning.
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