Nerlnet is a distributed machine learning platform for experiments and IoT deployment.
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Updated
May 22, 2024 - Python
Nerlnet is a distributed machine learning platform for experiments and IoT deployment.
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.
Framework that supports pipeline federated split learning with multiple hops.
Paddle with Decentralized Trust based on Xuperchain
Implementation of asynchronous federated learning in flower.
This is a bibliography survey upon Distributed Machine Learning. The survey contains algorithmic selections and architectures that can facilitate distributed learning on ML models. There is also a part that presents MLlib, a ML library from Apache Spark for distributed ML implementations.
GeoMX: A fast and unified system for distributed machine learning over geo-distributed data centers.
Comparison of distributed machine learning techniques applied to openly available datasets
This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
[arXiv] Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations
Python module for simulating gossip learning.
A quick library for Distributed Machine Learning which includes a matrix-math utils library built around CUDA kernels.
Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
CSCE 585 - Machine Learning Systems
A PS ML training architecture with p4 programmable switches.
🔨 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.
Materials for "Machine Learning on Big Data" course
[NeurIPS 2022] SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
[ICME 2023] Semi-Supervised Federated Learing for Keyword Spotting
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