Collaborative Machine Learning (CML) allows participants to jointly train a
machine learning model while keeping their training data private. In scenarios
where privacy is a strong requirement, such as health-related applications,
safety is also a primary concern. This means that privacy-preserving CML
processes must produce models that output correct and reliable decisions
\emph{even in the presence of potentially untrusted participants}. In response
to this issue, researchers propose to use \textit{robust aggregators} that rely
on metrics which help filter out malicious contributions that could compromise
the training process. In this work, we formalize the landscape of robust
aggregators in the literature. Our formalization allows us to show that
existing robust aggregators cannot fulfill their goal: either they use
distance-based metrics that cannot accurately identify targeted malicious
updates; or propose methods whose success is in direct conflict with the
ability of CML participants to learn from others and therefore cannot eliminate
the risk of manipulation without preventing learning.

合作机器学习允许参与者在保护私人训练数据的同时，共同训练一个机器学习模型。研究人员提出使用强大的聚合器来过滤可能危及训练过程的恶意贡献，但我们的研究表明现有的强大的聚合器无法实现其目标，因为它们要么使用无法准确识别恶意更新的基于距离的指标，要么提出的方法与参与者从其他人那里学习的能力相冲突，因此无法排除操纵的风险。

协作机器学习中的稳健性与学习之间的冲突

On the Conflict of Robustness and Learning in Collaborative Machine  Learning

Federated Learning (FL) enables collaborative training of a Machine Learning
(ML) model across multiple parties, facilitating the preservation of users' and
institutions' privacy by keeping data stored locally. Instead of centralizing
raw data, FL exchanges locally refined model parameters to build a global model
incrementally. While FL is more compliant with emerging regulations such as the
European General Data Protection Regulation (GDPR), ensuring the right to be
forgotten in this context - allowing FL participants to remove their data
contributions from the learned model - remains unclear. In addition, it is
recognized that malicious clients may inject backdoors into the global model
through updates, e.g. to generate mispredictions on specially crafted data
examples. Consequently, there is the need for mechanisms that can guarantee
individuals the possibility to remove their data and erase malicious
contributions even after aggregation, without compromising the already acquired
"good" knowledge. This highlights the necessity for novel Federated Unlearning
(FU) algorithms, which can efficiently remove specific clients' contributions
without full model retraining. This survey provides background concepts,
empirical evidence, and practical guidelines to design/implement efficient FU
schemes. Our study includes a detailed analysis of the metrics for evaluating
unlearning in FL and presents an in-depth literature review categorizing
state-of-the-art FU contributions under a novel taxonomy. Finally, we outline
the most relevant and still open technical challenges, by identifying the most
promising research directions in the field.

本研究调查了联邦学习中取消学习的背景概念、经验证据和实际指南，提出了一种新的分类法对最先进的取消学习算法进行详细分析，并确定了该领域最有前景的研究方向和尚未解决的技术挑战。