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A federated learning framework to support scalable and reproducible research
Official code implementation for "Personalized Federated Learning using Hypernetworks" [ICML 2021]
nips23-Dynamic Personalized Federated Learning with Adaptive Differential Privacy
Paper notes and code for differentially private machine learning
Code for visualizing the loss landscape of neural nets
Differentially Private Federated Learning on Heterogeneous Data
This repository contains the official implementation for the manuscript: Make Landscape Flatter in Differentially Private Federated Learning (2023 CVPR)
这是一款提高ChatGPT的数据安全能力和效率的插件。并且免费共享大量创新功能，如：自动刷新、保持活跃、数据安全、取消审计、克隆对话、言无不尽、净化页面、展示大屏、拦截跟踪、日新月异、明察秋毫等。让我们的AI体验无比安全、顺畅、丝滑、高效、简洁。
ICCV 2023 accepted paper, GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
Code Implementation and Informations about FedAS
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
Similarity Guided Model Aggregation for Federated Learning
Benchmark of federated learning. Dedicated to the community. 🤗
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
jhoon-oh / FedBABU
Forked from pliang279/LG-FedAvgICLR 2022, "FedBABU: Toward Enhanced Representation for Federated Image Classification"
小红书笔记 | 评论爬虫、抖音视频 | 评论爬虫、快手视频 | 评论爬虫、B 站视频 ｜ 评论爬虫、微博帖子 ｜ 评论爬虫、百度贴吧帖子 ｜ 百度贴吧评论回复爬虫 | 知乎问答文章｜评论爬虫
PyTorch implementation of Layer-wised Model Aggregation for Personalized Federated Learning
AAAI 2023 accepted paper, FedALA: Adaptive Local Aggregation for Personalized Federated Learning
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. www.pfllib.com/
KDD 2023 accepted paper, FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。