TTitcombe / NoPeekNN

Related projects: ⓘ

• TTitcombe / Model-Inversion-SplitNN
  Research into model inversion on SplitNN
  ☆14Updated 7 months ago
• s-huu / Ditto
  ☆54Updated 3 years ago
• jeremy313 / non-iid-dataset-for-personalized-federated-learning
  Official implementation of "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective"…
  ☆79Updated 4 years ago
• inspire-group / ModelPoisoning
  Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
  ☆147Updated last year
• pasquini-dario / SplitNN_FSHA
  Privacy attacks on Split Learning
  ☆37Updated 2 years ago
• zechenghe / Inverse_Collaborative_Inference
  ☆38Updated 3 years ago
• ebagdasa / federated_adaptation
  Salvaging Federated Learning by Local Adaptation
  ☆55Updated last month
• lee-man / federated-learning
  Related material on Federated Learning
  ☆27Updated 4 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆52Updated last year
• thecml / dpsgd-optimizer
  Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…
  ☆37Updated 5 months ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆69Updated 3 years ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 5 years ago
• ebagdasa / pytorch-privacy
  simple Differential Privacy in PyTorch
  ☆48Updated 4 years ago
• Koukyosyumei / Attack_SplitNN
  reveal the vulnerabilities of SplitNN
  ☆30Updated 2 years ago
• lingjuanlv / FPPDL
  code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"
  ☆31Updated 2 years ago
• LPD-EPFL / ByzantineMomentum
  Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent (ICLR 2021)
  ☆19Updated 3 years ago
• krishnap25 / simplicial-fl
  Simplicial-FL to manage client device heterogeneity in Federated Learning
  ☆21Updated last year
• PatrickZH / Improved-Deep-Leakage-from-Gradients
  The code for "Improved Deep Leakage from Gradients" (iDLG).
  ☆139Updated 3 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆129Updated last year
• JunyiZhu-AI / R-GAP
  R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]
  ☆33Updated last year
• mkucz95 / encrypted_ai_finance
  Credit Approval Classification Deep Learning Model using Differential Drivacy, Secure Multi-Party Computation, and Federated Learning
  ☆24Updated last year
• spring-epfl / mia
  A library for running membership inference attacks against ML models
  ☆137Updated last year
• yw3xs / PPDL
  privacy preserving deep learning
  ☆15Updated 7 years ago
• gdisag / gradient_disaggregation
  ☆14Updated 8 months ago
• gaoliang13 / FedDC
  An implementation for "Federated Learning with Non-IID Data via Local Drift Decoupling and Correction"
  ☆77Updated 2 years ago
• datafleets / horizontal-federated-learning-blog
  Code for Horizontal Federated Learning blog around Credit Scoring
  ☆9Updated 4 years ago
• maxencenoble / Differential-Privacy-for-Heterogeneous-Federated-Learning
  Differentially Private Federated Learning on Heterogeneous Data
  ☆56Updated 2 years ago
• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆29Updated 2 years ago
• haiphanNJIT / PrivateDeepLearning
  Differential Privacy Preservation in Deep Learning under Model Attacks
  ☆131Updated 3 years ago
• ftramer / Handcrafted-DP
  ☆77Updated 2 years ago