Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks

• URL: http://arxiv.org/abs/2508.07676v1
• Date: Mon, 11 Aug 2025 06:53:32 GMT
• Title: Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks
• Authors: Chenchen Lin, Xuehe Wang,
• Abstract summary: Federated learning enables collaborative model training across decentralized clients without sharing local data.<n>A client's privacy loss depends not only on its privacy protection strategy but also on the privacy decisions of others.<n>We propose a socially-aware privacy-preserving FL mechanism that systematically quantifies indirect privacy leakage.
• Score: 1.5653612447564105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data, thereby enhancing privacy and facilitating collaboration among clients connected via social networks. However, these social connections introduce privacy externalities: a client's privacy loss depends not only on its privacy protection strategy but also on the privacy decisions of others, propagated through the network via multi-hop interactions. In this work, we propose a socially-aware privacy-preserving FL mechanism that systematically quantifies indirect privacy leakage through a multi-hop propagation model. We formulate the server-client interaction as a two-stage Stackelberg game, where the server, as the leader, optimizes incentive policies, and clients, as followers, strategically select their privacy budgets, which determine their privacy-preserving levels by controlling the magnitude of added noise. To mitigate information asymmetry in networked privacy estimation, we introduce a mean-field estimator to approximate the average external privacy risk. We theoretically prove the existence and convergence of the fixed point of the mean-field estimator and derive closed-form expressions for the Stackelberg Nash Equilibrium. Despite being designed from a client-centric incentive perspective, our mechanism achieves approximately-optimal social welfare, as revealed by Price of Anarchy (PoA) analysis. Experiments on diverse datasets demonstrate that our approach significantly improves client utilities and reduces server costs while maintaining model performance, outperforming both Social-Agnostic (SA) baselines and methods that account for social externalities.

Related papers

• Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy [6.517767018830131]
  We propose GDPFed, which partitions clients into groups based on their privacy budgets and achieves client-level DP within each group to reduce privacy budget waste.<n>We also introduce GDPFed$+$, which integrates model sparsification to eliminate unnecessary noise and optimize per-group client sampling ratios.
  arXiv  Detail & Related papers  (2025-05-19T18:55:34Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning [3.9166000694570076]
  Decentralized learning (DL) offers a novel paradigm in machine learning by distributing training across clients without central aggregation. DL's peer-to-peer model raises challenges in protecting against inference attacks and privacy leaks. This work proposes three secret sharing-based dropout resilience approaches for privacy-preserving DL.
  arXiv  Detail & Related papers  (2024-04-27T19:17:02Z)
• Secure Aggregation is Not Private Against Membership Inference Attacks [66.59892736942953]
  We investigate the privacy implications of SecAgg in federated learning. We show that SecAgg offers weak privacy against membership inference attacks even in a single training round. Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection.
  arXiv  Detail & Related papers  (2024-03-26T15:07:58Z)
• Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation [79.64626707978418]
  We propose using link local differential privacy over decentralized nodes to train graph neural networks. Our approach spends the privacy budget separately on links and degrees of the graph for the server to better denoise the graph topology. Our approach outperforms existing methods in terms of accuracy under varying privacy budgets.
  arXiv  Detail & Related papers  (2023-09-06T17:53:31Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
  We investigate the privacy-preserving joint edge association and power allocation problem. The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
  arXiv  Detail & Related papers  (2023-01-26T10:09:23Z)
• Social-Aware Clustered Federated Learning with Customized Privacy Preservation [38.00035804720786]
  We propose a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.
  arXiv  Detail & Related papers  (2022-12-25T10:16:36Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Group privacy for personalized federated learning [4.30484058393522]
  Federated learning is a type of collaborative machine learning, where participating clients process their data locally, sharing only updates to the collaborative model. We propose a method to provide group privacy guarantees exploiting some key properties of $d$-privacy.
  arXiv  Detail & Related papers  (2022-06-07T15:43:45Z)
• Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs [30.58690911428577]
  We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements. We develop (optimal) communication-efficient schemes for private mean estimation for several $ell_p$ spaces. We demonstrate that one can get the same privacy, optimization-performance operating point developed in recent methods that use full-precision communication.
  arXiv  Detail & Related papers  (2020-08-17T09:41:04Z)

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.