Federated Learning (FL) represents a promising approach to typical privacy
concerns associated with centralized Machine Learning (ML) deployments. Despite
its well-known advantages, FL is vulnerable to security attacks such as
Byzantine behaviors and poisoning attacks, which can significantly degrade
model performance and hinder convergence. The effectiveness of existing
approaches to mitigate complex attacks, such as median, trimmed mean, or Krum
aggregation functions, has been only partially demonstrated in the case of
specific attacks. Our study introduces a novel robust aggregation mechanism
utilizing the Fourier Transform (FT), which is able to effectively handling
sophisticated attacks without prior knowledge of the number of attackers.
Employing this data technique, weights generated by FL clients are projected
into the frequency domain to ascertain their density function, selecting the
one exhibiting the highest frequency. Consequently, malicious clients' weights
are excluded. Our proposed approach was tested against various model poisoning
attacks, demonstrating superior performance over state-of-the-art aggregation
methods.

我们的研究引入了一种新颖的健壮汇聚机制，利用傅里叶变换，能够有效应对复杂攻击，包括恶意行为和模型污染攻击，并且在各种模型污染攻击中表现出优越性能。

FedRDF：面向联邦学习中的毒化攻击的强大动态集成函数

FedRDF: A Robust and Dynamic Aggregation Function against Poisoning  Attacks in Federated Learning

A molecule's 2D representation consists of its atoms, their attributes, and
the molecule's covalent bonds. A 3D (geometric) representation of a molecule is
called a conformer and consists of its atom types and Cartesian coordinates.
Every conformer has a potential energy, and the lower this energy, the more
likely it occurs in nature. Most existing machine learning methods for
molecular property prediction consider either 2D molecular graphs or 3D
conformer structure representations in isolation. Inspired by recent work on
using ensembles of conformers in conjunction with 2D graph representations, we
propose E(3)-invariant molecular conformer aggregation networks. The method
integrates a molecule's 2D representation with that of multiple of its
conformers. Contrary to prior work, we propose a novel 2D--3D aggregation
mechanism based on a differentiable solver for the \emph{Fused
Gromov-Wasserstein Barycenter} problem and the use of an efficient online
conformer generation method based on distance geometry. We show that the
proposed aggregation mechanism is E(3) invariant and provides an efficient GPU
implementation. Moreover, we demonstrate that the aggregation mechanism helps
to outperform state-of-the-art property prediction methods on established
datasets significantly.

通过集成分子的 2D 表示和其多个构象体的表示，提出了一种新颖的 2D-3D 聚合机制，结合了分子的属性预测和机器学习方法，在已有数据集上显著优于现有的方法。

E (3)- 不变分子构象聚合网络

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

Federated learning (FL) is a collaborative learning paradigm allowing
multiple clients to jointly train a model without sharing their training data.
However, FL is susceptible to poisoning attacks, in which the adversary injects
manipulated model updates into the federated model aggregation process to
corrupt or destroy predictions (untargeted poisoning) or implant hidden
functionalities (targeted poisoning or backdoors). Existing defenses against
poisoning attacks in FL have several limitations, such as relying on specific
assumptions about attack types and strategies or data distributions or not
sufficiently robust against advanced injection techniques and strategies and
simultaneously maintaining the utility of the aggregated model. To address the
deficiencies of existing defenses, we take a generic and completely different
approach to detect poisoning (targeted and untargeted) attacks. We present
FreqFed, a novel aggregation mechanism that transforms the model updates (i.e.,
weights) into the frequency domain, where we can identify the core frequency
components that inherit sufficient information about weights. This allows us to
effectively filter out malicious updates during local training on the clients,
regardless of attack types, strategies, and clients' data distributions. We
extensively evaluate the efficiency and effectiveness of FreqFed in different
application domains, including image classification, word prediction, IoT
intrusion detection, and speech recognition. We demonstrate that FreqFed can
mitigate poisoning attacks effectively with a negligible impact on the utility
of the aggregated model.

FreqFed 是一种新颖的聚合机制，通过将模型更新转换到频域，可以有效过滤掉恶意攻击，无论攻击类型、策略和客户端数据分布，从而在不影响聚合模型的实用性的前提下有效缓解毒化攻击。

FreqFed: 基于频率分析的联邦学习中缓解污染攻击的方法

FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning  Attacks in Federated Learning

While much progress has been made in how to encode a text sequence into a
sequence of vectors, less attention has been paid to how to aggregate these
preceding vectors (outputs of RNN/CNN) into fixed-size encoding vector.
Usually, a simple max or average pooling is used, which is a bottom-up and
passive way of aggregation and lack of guidance by task information. In this
paper, we propose an aggregation mechanism to obtain a fixed-size encoding with
a dynamic routing policy. The dynamic routing policy is dynamically deciding
that what and how much information need be transferred from each word to the
final encoding of the text sequence. Following the work of Capsule Network, we
design two dynamic routing policies to aggregate the outputs of RNN/CNN
encoding layer into a final encoding vector. Compared to the other aggregation
methods, dynamic routing can refine the messages according to the state of
final encoding vector. Experimental results on five text classification tasks
show that our method outperforms other aggregating models by a significant
margin. Related source code is released on our github page.

本文提出一种聚合机制来获取具有动态路由策略的固定大小的向量表示文本序列，并通过实验结果在五个文本分类任务中展现了其优越性。