kenziyuliu / pets-challengeLinks
☆11Updated last year
Alternatives and similar repositories for pets-challenge
Users that are interested in pets-challenge are comparing it to the libraries listed below
Sorting:
- [NeurIPS 2022] JAX/Haiku implementation of "On Privacy and Personalization in Cross-Silo Federated Learning"☆27Updated 2 years ago
- ☆15Updated 2 years ago
- Simplicial-FL to manage client device heterogeneity in Federated Learning☆22Updated 2 years ago
- [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…☆36Updated 4 years ago
- Private Adaptive Optimization with Side Information (ICML '22)☆16Updated 3 years ago
- Federated Learning with Partial Model Personalization☆42Updated 3 years ago
- ☆80Updated 3 years ago
- Official repo for the paper: Recovering Private Text in Federated Learning of Language Models (in NeurIPS 2022)☆61Updated 2 years ago
- ☆40Updated 2 years ago
- ☆27Updated 3 years ago
- Federated Learning Framework Benchmark (UniFed)☆49Updated 2 years ago
- ☆10Updated 3 years ago
- Algorithms for Privacy-Preserving Machine Learning in JAX☆135Updated 2 weeks ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆32Updated 3 years ago
- [CVPRW 2023] "Many-Task Federated Learning: A New Problem Setting and A Simple Baseline" by Ruisi Cai, Xiaohan Chen, Shiwei Liu, Jayanth …☆13Updated 2 years ago
- Official code for "Personalized Federated Learning through Local Memorization" (ICML'22)☆44Updated 2 years ago
- R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]☆37Updated 2 years ago
- This repo implements several algorithms for learning with differential privacy.☆111Updated 3 years ago
- Code for Auditing DPSGD☆37Updated 3 years ago
- ☆19Updated 2 years ago
- ☆12Updated 4 years ago
- ☆23Updated 3 years ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆33Updated 4 years ago
- Salvaging Federated Learning by Local Adaptation☆56Updated last year
- FedDANE: A Federated Newton-Type Method (Asilomar Conference on Signals, Systems, and Computers ‘19)☆26Updated 2 years ago
- ☆18Updated 3 years ago
- Learning from history for Byzantine Robustness☆25Updated 4 years ago
- A repository for LotteryFL re-implementation and experiments☆13Updated 5 years ago
- Federated posterior averaging implemented in JAX☆52Updated 2 years ago
- Practical One-Shot Federated Learning for Cross-Silo Setting☆41Updated 4 years ago