Overcoming Gradient Inversion Challenges in Federated Learning: The DAGER Algorithm for Exact Text Reconstruction

Title: Overcoming Privacy Challenges in Federated Learning with DAGER Algorithm Federated learning allows multiple parties to collaborate on model training without sharing private data. However, privacy can be compromised by gradient inversion attacks. The DAGER algorithm, developed by researchers from INSAIT, Sofia University, ETH Zurich, and LogicStar.ai, addresses this challenge by precisely reconstructing entire batches of input text. It outperforms previous attacks in terms of speed, scalability, and reconstruction quality, supporting large batches and sequences for encoder and decoder transformers. DAGER leverages the rank deficiency of the gradient matrix of self-attention layers to efficiently reconstruct full input sequences. It progressively extends partial sequences with verified tokens, demonstrating superior performance compared to previous methods. The algorithm achieves near-perfect sequence reconstructions and showcases scalability and effectiveness in diverse scenarios. For AI KPI management advice, contact us at hello@itinai.com. To stay updated on leveraging AI, follow us on Telegram or Twitter. Practical AI Solution Spotlight: Explore the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Useful Links: - AI Lab in Telegram @itinai – free consultation - Twitter – @itinaicom