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.

提出 FLVoogd 联邦学习方法，采用密度聚类算法以及测试性距离控制等方法实现安全隐私保护，同时利用安全多方计算实现加密运算，在图像分类数据集中可以有效抵御恶意攻击，避免服务器数据泄露。

FLVoogd: 强健和隐私保护的联邦学习

FLVoogd: Robust And Privacy Preserving Federated Learning

Among the different modalities to assess emotion, electroencephalogram (EEG),
representing the electrical brain activity, achieved motivating results over
the last decade. Emotion estimation from EEG could help in the diagnosis or
rehabilitation of certain diseases. In this paper, we propose a dual model
considering two different representations of EEG feature maps: 1) a sequential
based representation of EEG band power, 2) an image-based representation of the
feature vectors. We also propose an innovative method to combine the
information based on a saliency analysis of the image-based model to promote
joint learning of both model parts. The model has been evaluated on four
publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results
outperform results from state-of-the-art approaches for three of the proposed
datasets with a lower standard deviation that reflects higher stability. For
sake of reproducibility, the codes and models proposed in this paper are
available at this https URL.

本文提出了一种双模型方法，结合了基于序列的 EEG 幅值表示和基于图像的表示。采用基于显著性分析的图像模型，促进了两个模型部分的联合学习。在四个公开数据集上进行评估，结果表明本方法优于现有方法，在情感估计任务上取得了较高的稳定性。