Federated Learning (FL) enables collaborative deep learning training across
multiple participants without exposing sensitive personal data. However, the
distributed nature of FL and the unvetted participants' data makes it
vulnerable to backdoor attacks. In these attacks, adversaries inject malicious
functionality into the centralized model during training, leading to
intentional misclassifications for specific adversary-chosen inputs. While
previous research has demonstrated successful injections of persistent
backdoors in FL, the persistence also poses a challenge, as their existence in
the centralized model can prompt the central aggregation server to take
preventive measures to penalize the adversaries. Therefore, this paper proposes
a methodology that enables adversaries to effectively remove backdoors from the
centralized model upon achieving their objectives or upon suspicion of possible
detection. The proposed approach extends the concept of machine unlearning and
presents strategies to preserve the performance of the centralized model and
simultaneously prevent over-unlearning of information unrelated to backdoor
patterns, making the adversaries stealthy while removing backdoors. To the best
of our knowledge, this is the first work that explores machine unlearning in FL
to remove backdoors to the benefit of adversaries. Exhaustive evaluation
considering image classification scenarios demonstrates the efficacy of the
proposed method in efficient backdoor removal from the centralized model,
injected by state-of-the-art attacks across multiple configurations.

该文提出了一种基于机器遗忘的方法，使得攻击者可以有效地去除联邦学习中注入的后门，该方法在保证集中模型性能的同时，防止不相关信息的过度遗忘，并通过图像分类方案的全面评估证明了其在多种攻击情境下有效去除后门。

远程消除后门：去除联邦学习中的痕迹

Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning

We design new differentially private algorithms for the Euclidean k-means
problem, both in the centralized model and in the local model of differential
privacy. In both models, our algorithms achieve significantly improved error
guarantees than the previous state-of-the-art. In addition, in the local model,
our algorithm significantly reduces the number of interaction rounds.
Although the problem has been widely studied in the context of differential
privacy, all of the existing constructions achieve only super constant
approximation factors. We present, for the first time, efficient private
algorithms for the problem with constant multiplicative error. Furthermore, we
show how to modify our algorithms so they compute private corsets for k-means
clustering in both models.

本研究针对欧几里得 k 均值问题，设计了新的差分隐私算法，其在中心模型和本地模型中均获得了显著提高的误差保证，并且还能计算私有 corsets 来处理 k 均值聚类问题。