GenDRA: Generative Data Reconstruction Attacks on Federated Edge Learning and Countermeasures

Federated edge learning (FEL) unites the decentralized training capabilities of multiple edge nodes to allow model gradient sharing and parameter aggregation across a peer-to-peer network. However, many intrinsic policy conflicts still exist in FEL, for example, the open accessibility of gradients will lead to the privacy leakage risk during the federal aggregation process. In this paper, we first identify that malicious users weaponized with generative artificial intelligence (GenAI) can generate fake samples that are almost identical to FEL participants’ training data. By analyzing how different configurations of GenAI affect attack effectiveness, we find that an adversary with strong patchwork and reconstruction capabilities can stealthily steal diverse training data from nearly all FEL participants. To thwart such a generative data reconstruction attack (GenDRA) scheme, we propose a novel target semantic dissolution (TSD) mechanism for enhancing the privacy-preserving ability of FEL, which encrypts only a very small number (≤10%) of gradient values in each training round that have a significant impact on human visual formation using format-preserving encryption. With TSD, the speculator cannot obtain a fake sample that is visually similar to the training sample because real gradients are actively concealed. Extensive experiments based on four benchmark datasets are performed to demonstrate the huge threat of GenAI and the effectiveness of TSD in all aspects: compelling accuracy performance, strong visual privacy guarantee, and low computing overhead.