  Federated Learning (FL) is a distributed learning paradigm that enables
multiple clients to collaborate on building a machine learning model without
sharing their private data. Although FL is considered privacy-preserved by
design, recent data reconstruction attacks demonstrate that an attacker can
recover clients' training data based on the parameters shared in FL. However,
most existing methods fail to attack the most widely used horizontal Federated
Averaging (FedAvg) scenario, where clients share model parameters after
multiple local training steps. To tackle this issue, we propose an
interpolation-based approximation method, which makes attacking FedAvg
scenarios feasible by generating the intermediate model updates of the clients'
local training processes. Then, we design a layer-wise weighted loss function
to improve the data quality of reconstruction. We assign different weights to
model updates in different layers concerning the neural network structure, with
the weights tuned by Bayesian optimization. Finally, experimental results
validate the superiority of our proposed approximate and weighted attack (AWA)
method over the other state-of-the-art methods, as demonstrated by the
substantial improvement in different evaluation metrics for image data
reconstructions.
