Privacy-enhancing and robust backdoor defense for federated learning on heterogeneous data

• Main Authors: CHEN, Zekai, YU, Shengxing, FAN, Mingyuan, LIU, Ximeng, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Federate learning; backdoor defense; distributed backdoor attack; privacy-preserving; heterogeneity data; Information Security; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8631; https://doi.org/10.1109/TIFS.2023.3326983
• Institution: Singapore Management University

Federated learning (FL) allows multiple clients to train deep learning models collaboratively while protecting sensitive local datasets. However, FL has been highly susceptible to security for federated backdoor attacks (FBA) through injecting triggers and privacy for potential data leakage from upl...