Alternatives and similar repositories for Learning-to-Detect-Malicious-Clients-for-Robust-FL

Users that are interested in Learning-to-Detect-Malicious-Clients-for-Robust-FL are comparing it to the libraries listed below

• jeremy313 / FL-WBC
  Official implementation of "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective"…
  ☆42Updated 3 years ago
• zaixizhang / FLDetector
  The official code of KDD22 paper "FLDetecotor: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clien…
  ☆84Updated 2 years ago
• jgshu / Attacks-and-Defenses-in-Federated-Learning
  ☆39Updated 4 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 5 years ago
• mattbev / 6.867-final-project
  Adversarial attacks and defenses against federated learning.
  ☆19Updated 2 years ago
• cpwan / Attack-Adaptive-Aggregation-in-Federated-Learning
  This is the code for our paper `Robust Federated Learning with Attack-Adaptive Aggregation' accepted by FTL-IJCAI'21.
  ☆45Updated 2 years ago
• vrt1shjwlkr / NDSS21-Model-Poisoning
  Code for NDSS 2021 Paper "Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses Against Federated Learning"
  ☆147Updated 3 years ago
• papersdclub / Differentially_private_federated_learning
  Differentially Private Federated Learning: A Client Level Perspective
  ☆12Updated 6 years ago
• YMJS-Irfan / DP-FedSAM
  This repository contains the official implementation for the manuscript: Make Landscape Flatter in Differentially Private Federated Lear…
  ☆51Updated 2 years ago
• SliencerX / Learning-to-Attack-Federated-Learning
  ☆21Updated 2 years ago
• jinghuichen / Focused-Flip-Federated-Backdoor-Attack
  Github Repo for AAAI 2023 paper: On the Vulnerability of Backdoor Defenses for Federated Learning
  ☆40Updated 2 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆73Updated 4 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆58Updated 2 years ago
• abogdanova / FL-MIA
  Federated Learning and Membership Inference Attacks experiments on CIFAR10
  ☆22Updated 5 years ago
• BHui97 / PrivateFL
  ☆42Updated 2 years ago
• Guncuke / flame-Taming-backdoor
  ☆24Updated last year
• zhmzm / FLTrust_pytorch
  ☆39Updated last year
• ksreenivasan / OOD_Federated_Learning
  ☆70Updated 3 years ago
• s-huu / Ditto
  ☆54Updated 4 years ago
• yjlee22 / byzantineFL
  PyTorch implementation of Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance
  ☆34Updated 10 months ago
• avdvg / VFL-Survey
  ☆35Updated 3 years ago
• FumiyukiKato / FL-attacks-papers
  A list of papers using/about Federated Learning especially malicious client and attacks.
  ☆12Updated 5 years ago
• XinyiYS / Robust-and-Fair-Federated-Learning
  Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in …
  ☆35Updated last year
• 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"
  ☆19Updated last year
• wanglun1996 / secure-robust-federated-learning
  ☆54Updated 2 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…
  ☆24Updated 10 months ago
• shaneson0 / attacking_federate_learning
  基于《A Little Is Enough: Circumventing Defenses For Distributed Learning》的联邦学习攻击模型
  ☆64Updated 5 years ago
• maxencenoble / Differential-Privacy-for-Heterogeneous-Federated-Learning
  Differentially Private Federated Learning on Heterogeneous Data
  ☆67Updated 3 years ago
• KaiyuanZh / FLIP
  [ICLR 2023, Best Paper Award at ECCV’22 AROW Workshop] FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
  ☆59Updated 8 months ago
• litian96 / ditto
  Ditto: Fair and Robust Federated Learning Through Personalization (ICML '21)
  ☆145Updated 3 years ago