DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

95.47% on CIFAR10 with PyTorch

Training and serving large-scale neural networks with auto parallelization.

Make huge neural nets fit in memory

Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.

Federated Learning Benchmark - Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)

The first open Federated Learning framework implemented in C++ and Python.

Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs

A library for federated learning (a distributed machine learning process) in an enterprise environment.

Wide Residual Networks (WideResNets) in PyTorch

Implementation of dp-based federated learning framework using PyTorch

ML model training for edge devices

Training neural networks in TensorFlow 2.0 with 5x less memory

Releasing the source code Version1.

EfficientNetV2 implementation using PyTorch

Investigating Split Learning and Federate Learning

a pytorch implement of mobileNet v2 on cifar10

EfficientNetV2 pytorch (pytorch lightning) implementation with pretrained model

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models. ICML 2021

[ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time

FedDCT: A Novel Federated Learning Approach for Training Large Convolutional Neural Networks

reveal the vulnerabilities of SplitNN

Code for ScaleFL

🎓Automatically Update Distributed Learning Papers Daily using Github Actions (Update Every 12th hours)

Applied Split Learning in PyTorch with torch.distributed.rpc and torch.distributed.autograd

Adaptive Resource-Aware Split-Learning, a framework for efficient model training in IoT systems