Approximate and Weighted Data Reconstruction Attack in Federated Learning

arXiv:2308.06822v2 Announce Type: replace
Abstract: 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 …

abstract arxiv attacks building cs.ai cs.cr cs.lg data design distributed distributed learning federated learning machine machine learning machine learning model math.oc multiple paradigm privacy private data training training data type