DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data

• URL: http://arxiv.org/abs/2606.04399v1
• Date: Wed, 03 Jun 2026 03:27:40 GMT
• Title: DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
• Abstract summary: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.<n>We propose a privacy-preserved decentralized learning algorithm with non-IID data, DPDL, which leverages the notion of Differential Privacy (DP) in cross-gradient aggregation.<n>Our rigorous theoretical analysis not only reveals the minimum noise level required to achieve a specific level of privacy preservation, but also illustrates that our algorithm still achieves a linear speedup in training with non-IID data.
• Score: 5.903492793014408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server. Although the power of collaboration has been verified by many state-of-the-art studies, it entails extensive gradient information exchanging among the agents and thus induces high risk of privacy leakage for the individual agents. Moreover, in real-world applications, the training data are usually non-identically and independently distributed across the agents, inducing more challenges to enable privacy-preserved decentralized learning. To address these issues, we propose a privacy-preserved decentralized learning algorithm with non-IID data, DPDL, which leverages the notion of Differential Privacy (DP) in cross-gradient aggregation through a similarity-based calibration technique. Specifically, in each round, each agent perturbs the cross-gradients (i.e., the derivatives of its neighbors' local model in its private local data) by Gaussian noise mechanism before sharing them with its neighbors; it then adopt cosine similarity to calibrate the received perturbed cross-gradients such that the aggregation of the calibrated cross-gradients can be utilized to effectively update local model in a momentum-like manner. Our rigorous theoretical analysis not only reveals the minimum noise level required to achieve a specific level of privacy preservation, but also illustrates that our algorithm still achieves a linear speedup in training with non-IID data. We finally conduct extensive experiments on real-world dataset to validate the effectiveness of our algorithm in defending privacy attacks and in training accurate models.

Related papers

• PDSL: Privacy-Preserved Decentralized Stochastic Learning with Heterogeneous Data Distribution [8.055697728504326]
  In decentralized learning, a group of agents collaborates to learn a global model using distributed datasets without a central server.<n>In this paper, we propose P, a novel privacy-preserved decentralized learning algorithm with heterogeneous data distribution.
  arXiv  Detail & Related papers  (2025-03-31T04:58:05Z)
• Federated Learning with Differential Privacy: An Utility-Enhanced Approach [12.614480013684759]
  Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data.<n>Recent studies have shown that federated learning alone does not guarantee privacy, as private data may still be inferred from the uploaded parameters to the central server.<n>We present a modification to these vanilla differentially private algorithms based on a Haar wavelet transformation step and a novel noise injection scheme that significantly lowers the bound of the noise variance.
  arXiv  Detail & Related papers  (2025-03-27T04:48:29Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus [11.414398732656839]
  We propose a framework that embeds differential privacy into decentralized deep learning and secures each agent's local dataset during and after cooperative training. We prove convergence guarantees for algorithms derived from this framework and demonstrate its practical utility when applied to subgradient and ADMM decentralized approaches.
  arXiv  Detail & Related papers  (2023-06-24T07:46:00Z)
• Neighborhood Gradient Clustering: An Efficient Decentralized Learning Method for Non-IID Data Distributions [5.340730281227837]
  The current state-of-the-art decentralized algorithms mostly assume the data distributions to be Independent and Identically Distributed. We propose textitNeighborhood Gradient Clustering (NGC), a novel decentralized learning algorithm that modifies the local gradients of each agent using self- and cross-gradient information.
  arXiv  Detail & Related papers  (2022-09-28T19:28:54Z)
• Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
  Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
  arXiv  Detail & Related papers  (2022-05-08T14:38:23Z)
• Cross-Gradient Aggregation for Decentralized Learning from Non-IID data [34.23789472226752]
  Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server. We propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm. We show superior learning performance of CGA over existing state-of-the-art decentralized learning algorithms.
  arXiv  Detail & Related papers  (2021-03-02T21:58:12Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Graph-Homomorphic Perturbations for Private Decentralized Learning [64.26238893241322]
  Local exchange of estimates allows inference of data based on private data. perturbations chosen independently at every agent, resulting in a significant performance loss. We propose an alternative scheme, which constructs perturbations according to a particular nullspace condition, allowing them to be invisible.
  arXiv  Detail & Related papers  (2020-10-23T10:35:35Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification [69.29602103582782]
  Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for person re-identification (Re-ID) We propose a novel paradigm called Federated Person Re-Identification (FedReID) to construct a generalisable global model (a central server) by simultaneously learning with multiple privacy-preserved local models (local clients) This client-server collaborative learning process is iteratively performed under privacy control, enabling FedReID to realise decentralised learning without sharing distributed data nor collecting any
  arXiv  Detail & Related papers  (2020-06-07T13:32:33Z)

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.