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nips23-Dynamic Personalized Federated Learning with Adaptive Differential Privacy
[CVPRW 2023] "Many-Task Federated Learning: A New Problem Setting and A Simple Baseline" by Ruisi Cai, Xiaohan Chen, Shiwei Liu, Jayanth Srinivasa, Myungjin Lee, Ramana Kompella, Zhangyang Wang
Code accompanying the paper "Disparate Impact in Differential Privacy from Gradient Misalignment".
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
code for paper 'Squeezing More Utility via Adaptive Clipping on Deferentially Private Gradients in Federated Meta-Learning' ACSAC 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.
Paper notes and code for differentially private machine learning
Codebase for An Efficient Framework for Clustered Federated Learning.
Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget
A large labelled image dataset for benchmarking in federated learning
Differentially Private Decentralized Stochastic Neighbor Embedding (dSNE) with AdaCliP
Learning rate adaptation for differentially private stochastic gradient descent
PyTorch implementation of Layer-wised Model Aggregation for Personalized Federated Learning
[CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long*, Luka Rimanic, Ce Zhang, Bo Li
Adaptive gradient sparsification for efficient federated learning: an online learning approach
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Local Differential Privacy for Federated Learning
Defense against Gradient Leakage Attack
PyTorch Implementation of Federated Reconstruction: Partially Local Federated Learning
[ICML 2022] "DisPFL: Towards Communication-Efficient Personalized Federated learning via Decentralized Sparse Training"