ABG: An Adaptive Block-based Gradient Inversion Attack in Federated Learning

Federated learning (FL) aims to protect training data by training models through gradient sharing without directly exchanging raw data. However, gradient inversion attacks (GIAs) demonstrate that shared gradients can still reveal sensitive client training data. In this paper, we proposes Adaptive Block-based Gradient inversion attack (ABG), an adaptive gradient inversion framework for high-fidelity image reconstruction. Unlike existing GIAs that rely on fixed reconstruction strategies and exhibit image-dependent reconstruction performance, ABG leverages the gradient mean to perform adaptive initialization and adaptively select the residual-layer weighting scheme. It further incorporates image encoding and decoding to reduce the pixel-level optimization space and performs block-wise gradient matching for ResNet architectures. Compared with the current SOTA approach, ABG improves PSNR by up to 5 dB while reducing reconstruction time by more than 12%. Extensive experiments across diverse settings validate the effectiveness and stability of the proposed adaptive strategy, highlighting the practical privacy risks of gradient leakage in FL.