DP-EMAR: A Differentially Private Framework for Autonomous Model Weight Repair in Federated IoT Systems
- URL: http://arxiv.org/abs/2512.13460v1
- Date: Mon, 15 Dec 2025 15:56:58 GMT
- Title: DP-EMAR: A Differentially Private Framework for Autonomous Model Weight Repair in Federated IoT Systems
- Authors: Chethana Prasad Kabgere, Shylaja S S,
- Abstract summary: Federated Learning (FL) enables decentralized model training without sharing raw data.<n>In multi tier Federated IoT (Fed-IoT) systems, unstable connectivity and adversarial interference can silently alter transmitted parameters.<n>We propose DP-EMAR, a differentially private, error model based autonomous repair framework that detects and reconstructs transmission induced distortions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
Related papers
- A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
arXiv Detail & Related papers (2026-02-23T16:12:02Z) - ERIS: Enhancing Privacy and Communication Efficiency in Serverless Federated Learning [6.486831630436399]
ERIS is a serverless FL framework that balances privacy and accuracy while eliminating the server bottleneck and distributing the communication load.<n>We theoretically prove that ERIS converges at the same rate as FedAvg under standard assumptions, and (ii) bounds mutual information leakage inversely with the number of aggregators.
arXiv Detail & Related papers (2026-02-09T13:05:41Z) - Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
arXiv Detail & Related papers (2026-02-01T05:54:59Z) - Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregation [5.905949608791961]
This paper introduces a semantics-driven federated IDS framework that incorporates language-derived semantic supervision into federated optimization.<n>The framework achieves over 80% zero-shot detection accuracy on unseen attack patterns, improving zero-day discrimination by more than 10% compared to similarity-based baselines.
arXiv Detail & Related papers (2026-01-30T16:38:05Z) - SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks [0.7724583352717439]
We propose a comprehensive and robust privacy-preserving federated learning (FL) framework tailored for intrusion detection in IoT networks.<n>SecureDyn-FL is designed to simultaneously address multiple security dimensions in FL-based IDS.<n>We show that SecureDyn- FL consistently outperforms state-of-the-art FL-based IDS defenses.
arXiv Detail & Related papers (2026-01-10T07:23:49Z) - ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation [8.934430272818048]
Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites.<n>ZTFed-MAS2S is a zero-trust federated learning framework that integrates a sequence-to-sequence imputation model.<n>Experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation.
arXiv Detail & Related papers (2025-08-24T01:50:58Z) - Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy [51.028047763426265]
Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information.<n> transmitting semantic-rich data over insecure channels introduces privacy risks.<n>This paper proposes a novel SemCom framework that integrates differential privacy mechanisms to protect sensitive semantic features.
arXiv Detail & Related papers (2025-04-23T08:42:44Z) - FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning [17.853502904387376]
Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems.<n>We propose Federated Error Minimization (FedEM), a novel algorithm that incorporates controlled perturbations through adaptive noise injection.<n> Experimental results on benchmark datasets demonstrate that FedEM significantly reduces privacy risks and preserves model accuracy, achieving a robust balance between privacy protection and utility preservation.
arXiv Detail & Related papers (2025-03-08T02:48:00Z) - Communication and Energy Efficient Wireless Federated Learning with
Intrinsic Privacy [16.305837225117603]
Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally.
We propose a novel wireless FL scheme called private edge learning with spars (PFELS) to provide client-level DP guarantee with intrinsic channel noise.
arXiv Detail & Related papers (2023-04-15T03:04:11Z) - Reliable Federated Disentangling Network for Non-IID Domain Feature [62.73267904147804]
In this paper, we propose a novel reliable federated disentangling network, termed RFedDis.
To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling.
Our proposed RFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.
arXiv Detail & Related papers (2023-01-30T11:46:34Z) - Federated Learning with Sparsified Model Perturbation: Improving
Accuracy under Client-Level Differential Privacy [27.243322019117144]
Federated learning (FL) enables distributed clients to collaboratively learn a shared statistical model.
sensitive information about the training data can still be inferred from model updates shared in FL.
Differential privacy (DP) is the state-of-the-art technique to defend against those attacks.
This paper develops a novel FL scheme named Fed-SMP that provides client-level DP guarantee while maintaining high model accuracy.
arXiv Detail & Related papers (2022-02-15T04:05:42Z) - Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy.
These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem.
We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
arXiv Detail & Related papers (2020-12-10T23:28:36Z) - Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users.
An adversary may still be able to infer the private training data by attacking the released model.
Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
arXiv Detail & Related papers (2020-05-01T04:28:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.