Reinforcement learning using PPO on the 3D Ball environment for Unity ML-Agents. Using MPI to do distributed training across separate processes
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Updated
Jul 2, 2021 - Python
Reinforcement learning using PPO on the 3D Ball environment for Unity ML-Agents. Using MPI to do distributed training across separate processes
Papers related to Federated Learning in all venue (dblp)
Training and deploying sentiment analysis models with deep learning using Amazon SageMaker. A BERT model was trained using distributed training with the help of Hugging Face.
CD-GraB is a distributed gradient balancing framework that aims to find distributed data permutation with provably better convergence guarantees than Distributed Random Reshuffling (D-RR). https://arxiv.org/pdf/2302.00845.pdf.
[WIP] elastic training implemented with MXNet
This repository is the code basis of the paper intitled "The learning costs of Federated Learning in constrained scenarios"
This repository is the code basis for the paper titled "Efficient Training: Federated Learning Cost Analysis"
The smartphones of the people probably carry the most valueable but also private data. Since using data promisses to be one of the best ways to fight back against COVID-19, it is highly desirable to get access. By using a Federated Learning approach with PySyft it is possible to learn from the private data right on the smartphone, with the data …
Speeding up DNN training for image classification, with OpenMPI
Collaborative Machine Learning approach to train a mode that classifies a person as smoker or non-smoker based on the user data. The distributed approach of training is done with secure model transmissions to central cloud location where Amazon EC2 instance aggregates the new model based on new training received in Homomorphically Encrypted forms
A federated learning simulator written in PyTorch, which supports a virtually unlimited number of clients.
CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning
Page for fedsim
Just a casual project that provides a minor solution to the distributed learning of block non-i.i.d. data
Codes and experiments for the paper "Max-Discrepancy Distributed Learning: Fast Risk Bounds and Algorithms"
Dist-DGL running on wsl2, minikube with single machine
BurrMill core
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