Alternatives and similar repositories for Proof-of-Learning

Users that are interested in Proof-of-Learning are comparing it to the libraries listed below

• pps-lab / rofl-project-code
  ☆33Updated 2 years ago
• LPD-EPFL / AggregaThor
  Byzantine-resilient distributed SGD with TensorFlow.
  ☆40Updated 4 years ago
• cleverhans-lab / verifiable-unlearning
  ☆20Updated 2 years ago
• ftramer / Handcrafted-DP
  ☆80Updated 3 years ago
• cleverhans-lab / capc-iclr
  CaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where b…
  ☆26Updated 3 years ago
• google-research / cryptanalytic-model-extraction
  ☆52Updated 4 years ago
• pps-lab / fl-analysis
  ☆29Updated 2 years ago
• jagielski / auditing-dpsgd
  Code for Auditing DPSGD
  ☆37Updated 3 years ago
• epfml / byzantine-robust-optimizer
  Learning from history for Byzantine Robustness
  ☆24Updated 4 years ago
• AI-secure / DataLens
  [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…
  ☆38Updated 3 years ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 6 years ago
• marcoszh / BatchCrypt
  ☆88Updated 5 years ago
• microsoft / prv_accountant
  A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.
  ☆73Updated last year
• kenziyuliu / private-cross-silo-fl
  [NeurIPS 2022] JAX/Haiku implementation of "On Privacy and Personalization in Cross-Silo Federated Learning"
  ☆27Updated 2 years ago
• TRUST-TUDa / SAFELearn
  Secure Aggregation for FL
  ☆35Updated last year
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆135Updated 2 years ago
• MinChen00 / UnlearningLeaks
  Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
  ☆48Updated 3 years ago
• yuxiangw / autodp
  autodp: A flexible and easy-to-use package for differential privacy
  ☆274Updated last year
• wanglun1996 / secure-robust-federated-learning
  ☆54Updated 2 years ago
• DistributedML / FoolsGold
  A sybil-resilient distributed learning protocol.
  ☆106Updated last year
• pasquini-dario / SplitNN_FSHA
  Privacy attacks on Split Learning
  ☆42Updated 3 years ago
• DachengLi1 / MPCFormer
  (ICLR 2023 Spotlight) MPCFormer: fast, performant, and private transformer inference with MPC
  ☆99Updated 2 years ago
• ebagdasa / differential-privacy-vs-fairness
  Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19
  ☆33Updated 4 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆73Updated 4 years ago
• csl-cqu / awesome-secure-federated-learning-papers
  ☆29Updated 2 years ago
• awslabs / fast-differential-privacy
  Fast, memory-efficient, scalable optimization of deep learning with differential privacy
  ☆125Updated 2 months ago
• Koukyosyumei / NAIST-FedML-Experiments
  ☆19Updated 3 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆57Updated 2 years ago
• vaikkunth / PrivacyFL
  A Simulator for Privacy Preserving Federated Learning
  ☆94Updated 4 years ago
• Hazelsuko07 / InstaHide
  InstaHide: Instance-hiding Schemes for Private Distributed Learning
  ☆50Updated 4 years ago