FedShop

Federated Query Benchmark

A benchmark for testing the scalability of SPARQL federation engines in e-commerce scenarios

Code for FedShop: The Federated Shop Benchmark

GitHub

8 stars
4 watching
2 forks
Language: Jupyter Notebook
last commit: 3 months ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

Repository Description Stars
dice-group/costfed An index-assisted federation engine for optimizing queries across multiple SPARQL endpoints 18
jinheonbaek/fed-pub Personalized Subgraph Federated Learning framework for distributed machine learning 44
talwalkarlab/leaf A benchmarking framework for federated machine learning tasks across various domains and datasets 851
gaoliang13/feddc Federated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift 79
bordoley/reactfsharp A proof-of-concept demo that implements a React-like declarative UI API for F# 0
federatedai/fate-test A collection of tools and tests for evaluating the performance of federated machine learning systems 1
goerlitz/rdffederator Federation infrastructure for distributed RDF data sources using SPARQL queries and statistical analysis of VoiD descriptions. 5
google-research/federated A collection of research projects exploring decentralized machine learning and analytics techniques 690
illidanlab/foster This project develops an approach to improve out-of-distribution detection in federated learning by leveraging data heterogeneity 18
jiayunz/fedalign Develops an alignment framework for federated learning with non-identical client class sets 4
yuetan031/fedproto An implementation of federated learning with prototype-based methods across heterogeneous clients 133
haozzh/fedcr Evaluates various methods for federated learning on different models and tasks. 17
unc-optimization/feddr An implementation of algorithms for decentralized machine learning in nonconvex optimization problems 8
omarfoq/fedem Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. 154
bodigrim/tasty-bench A lightweight benchmarking framework with a simple statistical model 80