Breaching privacy in federated learning scenarios for vision and text
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Updated
Jan 24, 2026 - Python
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
[TMLR] CoDeC: Communication-Efficient Decentralized Continual Learning
source code of AISTATS 2020 paper: Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Investigating the reproducibility of federated GNN models
[CVPR 2023] CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-friendly Distillation Loss
Fine-tuning multimodal models using Parallel Split Learning
A decentralized/P2P federated learning library
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.
A Test Bed for Prototyping Fully-Decentralized ML Experiments
subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment
Decentralized and Privacy-Preserving Machine Learning: Exploring the Power of Federated Learning.
code for "Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence"
FL-Interactive-Game: Interactive web game that teaches basic components of Federated Learning
This is the repository for our paper NTK-DFL, which uses the Neural Tangent Kernel to train models in a decentralized, federated setting.
This repository provides a framework for Federated Prognostics and Health Management (PHM) using Temporal Convolutional Networks (TCN) to predict the Remaining Useful Life (RUL) of industrial assets. Using the NASA CMAPSS dataset, it enables comparative studies between centralized training baselines and federated learning environments.
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