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Comparison b/w Federated Learning & Split Learning for credit card fraud detection dataset using Pytorch
Official repository for Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [CVPR 2022 Oral, Best Paper Finalist]
[ACM MobiCom 2022] "PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning" by Chenning Li, Xiao Zeng, Mi Zhang, and Zhichao Cao.
[TMLR'24] This repository includes the official implementation our paper "FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning"
[Official] NeurIPS 2023, "Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection"
AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
Official implementation of the paper "Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity"
"Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things" (IEEE) - Exploring resource-efficient & data privacy enabling training at the edge
Anomaly detection using Federated Learning with FLEX.
An open-source framework for machine learning and other computations on decentralized data.
Federated k-means clustering algorithm implementation and proof of concept.
Codebase for An Efficient Framework for Clustered Federated Learning.
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
Code release for Tackling Data Heterogeneity in Federated Learning with Class Prototypes appeared on AAAI2023.
Federated Learning (FL) experiment simulation in Python.
Federated learning used to detect credit card fraud
[NeurIPS 2023] "FedFed: Feature Distillation against Data Heterogeneity in Federated Learning"
Practical course about Large Language Models.
A high-level federated learning Python library used to run complex federated learning experiments at scale on a Substra network
1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
📋 A list of open LLMs available for commercial use.
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.