Breaching privacy in federated learning scenarios for vision and text

DISCO is a code-free and installation-free browser platform that allows any non-technical user to collaboratively train machine learning models without sharing any private data.

The official implementation of the paper "Topology-aware Generalization of Decentralized SGD"

[ICML 2023] Decentralized SGD and Average-direction SAM are Asymptotically Equivalent

[IEEE TSIPN' 2022] "Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks", by Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, and Brian M. Sadler

source code of AISTATS 2020 paper: Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

[CVPR 2023] CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-friendly Distillation Loss

[TMLR] CoDeC: Communication-Efficient Decentralized Continual Learning

Investigating the reproducibility of federated GNN models

code for "Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence"

Device-to-Device (D2D) and assocaited Federated Learning simulation in Python using Pytorch for Learning operations. Implemented simultaneous operation of multiple FL modes.

A blockchain-based ecosystem for Open Educational Resources (OERs) built on Ethereum. This project aims to establish standards and infrastructure for creating, sharing, and managing educational content using blockchain technology.

subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment

Decentralized and Privacy-Preserving Machine Learning: Exploring the Power of Federated Learning.

pytorch implementation of Optimization as a Model for Few-shot Learning

Handwritten Digit Classification using Federated Learning