yzeng58 / Improving-Fairness-via-Federated-Learning
☆13Updated 2 years ago
Alternatives and similar repositories for Improving-Fairness-via-Federated-Learning:
Users that are interested in Improving-Fairness-via-Federated-Learning are comparing it to the libraries listed below
- ☆20Updated 2 years ago
- Official code repository for our accepted work "Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning" in NeurI…☆22Updated 7 months ago
- ☆40Updated last year
- Papers related to federated learning in top conferences (2020-2024).☆68Updated 6 months ago
- ☆54Updated 3 years ago
- ☆16Updated last year
- Code accompanying the paper "Disparate Impact in Differential Privacy from Gradient Misalignment".☆11Updated 2 years ago
- ☆55Updated 2 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆56Updated 2 years ago
- For distributed machine learning☆28Updated this week
- Official code for "Personalized Federated Learning through Local Memorization" (ICML'22)☆43Updated last year
- Official implementation of our work "Collaborative Fairness in Federated Learning."☆51Updated 11 months ago
- Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in …☆35Updated 11 months ago
- ☆36Updated 3 years ago
- ☆18Updated 3 years ago
- Simplicial-FL to manage client device heterogeneity in Federated Learning☆22Updated last year
- ☆29Updated 2 years ago
- Official PyTorch implementation of DENSE (NeurIPS 2022)☆30Updated 2 years ago
- This repo implements several algorithms for learning with differential privacy.☆108Updated 2 years ago
- ☆30Updated 4 years ago
- ☆21Updated 3 years ago
- ☆15Updated last year
- Personalized Federated Learning via Variational Bayesian Inference [ICML 2022]☆56Updated 2 years ago
- Official Implementation of ICML'23 "Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting".☆14Updated last year
- ☆69Updated 2 years ago
- ☆38Updated 4 years ago
- (SIGKDD 2022) Connected Low-Loss Subspace Learning for a Personalization in Federated Learning (https://arxiv.org/abs/2109.07628)☆31Updated 9 months ago
- Diverse Client Selection for Federated Learning via Submodular Maximization☆30Updated 2 years ago
- The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".☆33Updated 2 years ago
- ☆14Updated last year