From: Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
 | Assumption | Goal | Limitation | ||
---|---|---|---|---|---|
Adversary | Active/Passive | Auxiliary data | |||
GAN attack (Hitaj et al. 2017) | Worker | Active | No | Classrepresentative inference | Allclassmembers similar |
CPA (Nasr et al. 2019) | Worker | Active/Passive | No | Membershipinference | Lackstheoretical proofofthebounds |
UFL (Melis et al. 2019) | Worker | Active/Passive | Yes | Propertiesinference | Auxiliarycondition maynotmeet |
DLGÂ (Zhu and Han 2020) | Server | Passive | No | Inferringtraining dataandlabel | Shallowand smoothnetworks |
iDLG (Zhao et al. 2020) | Server | Passive | No | Inferringtrainingdata withimagelabelrecovery | Asingleinput point |
Invert gradient (Geiping et al. 2020) | Server | Passive | No | Inferringtraining dataandlabel | Lowperformance atgeneralcase |
GradInversion (Yin et al. 2021) | Server | Passive | No | Largebatchimagerecovery forcomplexdatasets | Gradientsonlyupdate onceatlocal ineachiteration |
GRNN (Ren et al. 2021) | Server | Passive | No | Generatingtraining dataandlabel |