Materials for "Machine Learning on Big Data" course
-
Updated
Jul 23, 2023 - Jupyter Notebook
Materials for "Machine Learning on Big Data" course
This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
FedERA is a modular and fully customizable open-source FL framework, aiming to address these issues by offering comprehensive support for heterogeneous edge devices and incorporating both standalone and distributed computing. It includes new software modules to enhance usability and promote environ- mental sustainability.
Paddle with Decentralized Trust based on Xuperchain
Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
[ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".
🔨 A Flexible Federated Learning Simulator for Heterogeneous and Asynchronous.
GeoMX: A fast and unified system for distributed machine learning over geo-distributed data centers.
A curated list of Federated Learning papers/articles and recent advancements.
Python module for simulating gossip learning.
[NeurIPS 2022] SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
Solution for the Ultimate Student Hunt Challenge (1st place).
Distributed Bayesian Entity Resolution in Apache Spark
A distributed implementation of "Nested Subtree Hash Kernels for Large-Scale Graph Classification Over Streams" (ICDM 2012).
vector quantization for stochastic gradient descent.
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
🔨 A toolbox for federated learning, aiming to provide implementations of FedAvg, FedProx, Ditto, etc. in multiple versions, such as Pytorch/Tensorflow, single-machine/distributed, synchronized/asynchronous.
CSCE 585 - Machine Learning Systems
Atomo: Communication-efficient Learning via Atomic Sparsification
Add a description, image, and links to the distributed-machine-learning topic page so that developers can more easily learn about it.
To associate your repository with the distributed-machine-learning topic, visit your repo's landing page and select "manage topics."