Stars
Federated Learning Benchmark - Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)
This repository contains the official implementation for the manuscript: Make Landscape Flatter in Differentially Private Federated Learning (2023 CVPR)
Benchmark of federated learning. Dedicated to the community. 🤗
An easy-to-use federated learning platform
Implemented in Python, this project centers on a Privacy-Preserving Authentication System for Vehicular Ad-hoc Networks (VANET). Covering certificate generation and verification, it aims to boost V…
Breaching privacy in federated learning scenarios for vision and text
机器学习和差分隐私的论文笔记和代码仓
deep learning for image processing including classification and object-detection etc.
A curated list of resources for model inversion attack (MIA).
Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
Code & supplementary material of the paper Label Inference Attacks Against Federated Learning on Usenix Security 2022.
Code Repo for paper Label Leakage and Protection in Two-party Split Learning (ICLR 2022).
Reinforcement learning algorithms implemented for Tensorflow 2.0+ [DQN, DDPG, AE-DDPG, SAC, PPO, Primal-Dual DDPG]
Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation
FedScale is a scalable and extensible open-source federated learning (FL) platform.
Implementation of dp-based federated learning framework using PyTorch
Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )
计算机基础（计算机网络/操作系统/数据库/Git...）面试问题全面总结，包含详细的follow-up question以及答案；全部采用【问题+追问+答案】的形式，即拿即用，直击互联网大厂面试；可用于模拟面试、面试前复习、短期内快速备战面试...
【Java面试+Java后端技术学习指南】：一份通向理想互联网公司的面试指南，包括 Java，技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、分布式、数据库（MySQL、Redis）、Java 项目实战等
Simulate a federated setting and run differentially private federated learning.
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.