联邦学习中的近似加权数据重建攻击（AWA）方法
Approximate and Weighted Data Reconstruction Attack in Federated Learning
AI 导读
研究者提出针对联邦学习 FedAvg 场景的数据重建攻击方法 AWA（approximate and weighted attack）。该方法通过插值近似生成客户端本地训练的中间模型更新，并设计逐层加权损失函数、以贝叶斯优化调参来提升重建数据质量。实验表明 AWA 在多项图像数据重建评估指标上优于现有最先进方法，代码已开源。
正文 · 原文
Abstract:Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to collaborate on building a machine learning model without sharing their private data. Although FL is considered privacy-preserved by design, recent data reconstruction attacks demonstrate that an attacker can recover clients' training data based on the parameters shared in FL. However, most existing methods fail to attack the most widely used horizontal Federated Averaging (FedAvg) scenario, where clients share model parameters after multiple local training steps. To tackle this issue, we propose an interpolation-based approximation method, which makes attacking FedAvg scenarios feasible by generating the intermediate model updates of the clients' local training processes. Then, we design a layer-wise weighted loss function to improve the data quality of reconstruction. We assign different weights to model updates in different layers concerning the neural network structure, with the weights tuned by Bayesian optimization. Finally, experimental results validate the superiority of our proposed approximate and weighted attack (AWA) method over the other state-of-the-art methods, as demonstrated by the substantial improvement in different evaluation metrics for image data reconstructions. The source code is available at this https URL.
| Comments: | Funded by the European Union (HORIZON-MSCA-2021-DN-01-01 project ModConFlex, grant number 101073558). Code available at this https URL. Code DOI: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Optimization and Control (math.OC) |
| Cite as: | arXiv:2308.06822 [cs.LG] |
| (or arXiv:2308.06822v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2308.06822 arXiv-issued DOI via DataCite |
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| Journal reference: | IEEE Transactions on Big Data, vol. 12, no. 5, pp. 1606-1617, 2026 |
| Related DOI: | https://doi.org/10.1109/TBDATA.2026.3695416
DOI(s) linking to related resources |
Submission history
From: Ziqi Wang [view email]
[v1]
Sun, 13 Aug 2023 17:40:56 UTC (1,859 KB)
[v2]
Tue, 26 Mar 2024 19:39:23 UTC (1,384 KB)
[v3]
Fri, 15 May 2026 12:55:02 UTC (6,844 KB)
[v4]
Thu, 12 Nov 2026 16:04:54 UTC (6,842 KB)
来源：arXiv cs.AI · arxiv.org