Alternatives and similar repositories for CAFE:

Users that are interested in CAFE are comparing it to the libraries listed below

• xinjianluo / featureinference-vfl
  ☆24Updated 3 years ago
• fushuhao6 / Attack-Resistant-Federated-Learning
  ☆31Updated 4 years ago
• zlh-thu / DPFL
  A Fine-grained Differentially Private Federated Learning against Leakage from Gradients
  ☆13Updated 2 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆71Updated 3 years ago
• XinyiYS / Gradient-Driven-Rewards-to-Guarantee-Fairness-in-Collaborative-Machine-Learning
  Official code repository for our accepted work "Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning" in NeurI…
  ☆22Updated 4 months ago
• mattbev / 6.867-final-project
  Adversarial attacks and defenses against federated learning.
  ☆15Updated last year
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆55Updated last year
• wanglun1996 / secure-robust-federated-learning
  ☆54Updated 2 years ago
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆48Updated 2 years ago
• AI-secure / DataLens
  [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…
  ☆37Updated 3 years ago
• woodyx218 / opacus_global_clipping
  ☆13Updated last year
• zjysteven / PrivacyAttack_AT_FL
  [CVPRW'22] A privacy attack that exploits Adversarial Training models to compromise the privacy of Federated Learning systems.
  ☆12Updated 2 years ago
• zlijingtao / ResSFL
  Official Repository for ResSFL (accepted by CVPR '22)
  ☆21Updated 2 years ago
• jgshu / Attacks-and-Defenses-in-Federated-Learning
  ☆38Updated 3 years ago
• fmy266 / Pytorch-Backdoor-Unlearning
  Pytorch implementation of backdoor unlearning.
  ☆17Updated 2 years ago
• Machine-Learning-Security-Lab / mia_prune
  Membership Inference Attacks and Defenses in Neural Network Pruning
  ☆28Updated 2 years ago
• git-disl / CPL_attack
  ☆34Updated 3 years ago
• eth-sri / bayes-framework-leakage
  ☆10Updated 2 years ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆58Updated 4 months ago
• epfml / byzantine-robust-optimizer
  Learning from history for Byzantine Robustness
  ☆22Updated 3 years ago
• Mr-Ace-1997 / Backdoor-Attack-Against-Split-Neural-Network-Based-Vertical-Federated-Learning
  The code of the attack scheme in the paper "Backdoor Attack Against Split Neural Network-Based Vertical Federated Learning"
  ☆17Updated last year
• KaiyuanZh / FLIP
  [ICLR 2023, Best Paper Award at ECCV’22 AROW Workshop] FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
  ☆52Updated 2 months ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 5 years ago
• jhcknzzm / Federated-Learning-Backdoor
  ICML 2022 code for "Neurotoxin: Durable Backdoors in Federated Learning" https://arxiv.org/abs/2206.10341
  ☆68Updated last year
• JunyiZhu-AI / R-GAP
  R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]
  ☆34Updated 2 years ago
• ksreenivasan / OOD_Federated_Learning
  ☆68Updated 2 years ago
• cchoquette / membership-inference
  Code for the paper: Label-Only Membership Inference Attacks
  ☆64Updated 3 years ago
• TinfoilHat0 / Defending-Against-Backdoors-with-Robust-Learning-Rate
  The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".
  ☆30Updated 2 years ago
• lingjuanlv / FPPDL
  code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"
  ☆31Updated 2 years ago
• JingHongyi / Federated-Learning-Backdoor-Example-with-MNIST-and-CIFAR-10
  This is a simple backdoor model for federated learning.We use MNIST as the original data set for data attack and we use CIFAR-10 data set…
  ☆14Updated 4 years ago