Federated learning works by aggregating locally computed gradients from multiple clients, thus enabling collaborative training without sharing private client data. However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks. While these attacks perform well when applied on images, they are limited in the text domain and only permit approximate reconstruction of small batches and short input sequences. In this work, we propose DAGER, the first algorithm to recover whole batches of input text exactly. DAGER leverages the low-rank structure of self-attention layer gradients and the discrete nature of token embeddings to efficiently check if a given token sequence is part of the client data. We use this check to exactly recover full batches in the honest-but-curious setting without any prior on the data for both encoder- and decoder-based architectures using exhaustive heuristic search and a greedy approach, respectively. We provide an efficient GPU implementation of DAGER and show experimentally that it recovers full batches of size up to 128 on large language models (LLMs), beating prior attacks in speed (20x at same batch size), scalability (10x larger batches), and reconstruction quality (ROUGE-1/2 > 0.99).

DAGER是一种用于恢复文本数据批的算法，通过利用self-attention层的梯度的低秩结构和标记嵌入的离散性质，以精确的方式恢复整个批次的输入文本数据，用于编码器和解码器架构，并在实验中表现出超过0.99的ROUGE-1/2，速度比之前的攻击快了20倍，并且在可扩展性上可以处理10倍以上的批大小。

DAGER：大型语言模型的精确梯度反转