FLVoogd: Robust And Privacy Preserving Federated Learning

Yuhang Tian, Rui Wang, Yan Qiao, E. Panaousis, K. Liang
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引用次数: 2

Abstract

In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring sensitive personal information. Meanwhile, clients build dual models and perform test-based distance controlling to adjust their local models toward the global one to achieve personalizing. Our framework is automatic and adaptive that servers/clients don't need to tune the parameters during the training. In addition, our framework leverages Secure Multi-party Computation (SMPC) operations, including multiplications, additions, and comparison, where costly operations, like division and square root, are not required. Evaluations are carried out on some conventional datasets from the image classification field. The result shows that FLVoogd can effectively reject malicious uploads in most scenarios; meanwhile, it avoids data leakage from the server-side.
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FLVoogd:鲁棒和隐私保护联邦学习
在这项工作中,我们提出了FLVoogd,这是一种更新的联邦学习方法,其中服务器和客户端协同消除拜占庭攻击,同时保护隐私。特别是,服务器使用自动基于密度的噪声应用空间聚类(DBSCAN)与S2PC相结合,在不获取敏感个人信息的情况下对良性多数进行聚类。同时,客户建立双重模型,并进行基于测试的距离控制,将本地模型向全球模型调整,实现个性化。我们的框架是自动和自适应的,服务器/客户端不需要在训练期间调整参数。此外,我们的框架利用安全多方计算(SMPC)操作,包括乘法、加法和比较,这些操作不需要昂贵的操作,如除法和平方根。对图像分类领域的一些常规数据集进行了评价。结果表明,FLVoogd在大多数场景下都能有效地拒绝恶意上传;同时,避免了服务器端的数据泄露。
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