Federated Learning (FL) has garnered significant attention for its potential
to protect user privacy while enhancing model training efficiency. However,
recent research has demonstrated that FL protocols can be easily compromised by
active reconstruction attacks executed by dishonest servers. These attacks
involve the malicious modification of global model parameters, allowing the
server to obtain a verbatim copy of users' private data by inverting their
gradient updates. Tackling this class of attack remains a crucial challenge due
to the strong threat model. In this paper, we propose OASIS, a defense
mechanism based on image augmentation that effectively counteracts active
reconstruction attacks while preserving model performance. We first uncover the
core principle of gradient inversion that enables these attacks and
theoretically identify the main conditions by which the defense can be robust
regardless of the attack strategies. We then construct OASIS with image
augmentation showing that it can undermine the attack principle. Comprehensive
evaluations demonstrate the efficacy of OASIS highlighting its feasibility as a
solution.

本文提出了基于图像增强的防御机制 OASIS，以有效抵抗主动重构攻击，同时保持模型性能。

OASIS：联邦学习中的主动重构攻击抵消

OASIS: Offsetting Active Reconstruction Attacks in Federated Learning

Data is one of the most critical elements in building a large language model.
However, existing systems either fail to customize a corpus curation pipeline
or neglect to leverage comprehensive corpus assessment for iterative
optimization of the curation. To this end, we present a pretraining corpus
curation and assessment platform called Oasis -- a one-stop system for data
quality improvement and quantification with user-friendly interactive
interfaces. Specifically, the interactive modular rule filter module can devise
customized rules according to explicit feedback. The debiased neural filter
module builds the quality classification dataset in a negative-centric manner
to remove the undesired bias. The adaptive document deduplication module could
execute large-scale deduplication with limited memory resources. These three
parts constitute the customized data curation module. And in the holistic data
assessment module, a corpus can be assessed in local and global views, with
three evaluation means including human, GPT-4, and heuristic metrics. We
exhibit a complete process to use Oasis for the curation and assessment of
pretraining data. In addition, an 800GB bilingual corpus curated by Oasis is
publicly released.

数据是构建大规模语言模型的最关键元素之一，我们提出了一个名为 Oasis 的预训练数据整理和评估平台，通过用户友好的交互界面实现数据质量改进和量化评估，平台包括自定义数据整理模块和全面的数据评估模块，还发布了由 Oasis 整理的一个 800GB 双语语料库。