FLeet

Author: Georgios Damaskinos (georgios.damaskinos@gmail.com)

FLeet is a middleware introduced in FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction.

Quick Start

The following steps train a CNN on the non-IID MNIST dataset.

Setup
- Ubuntu 18.04.3 LTS
- gcc 7.5
- Python 3.6
- Java 1.8.0_77
- Maven 3.6.3
- Gradle 4.10.1
- Android API level 25
- Create and start an Android emulator
- Export variables:
  - JAVA_HOME
  - ANDROID_HOME
- Download Python numpy package
Download and extract the 4 files of the MNIST dataset

Compile, deploy and run FLeet

./local_deploy.sh 5554 9992 ~/log /path/to/mnist/ 0 0 1000 3000 0.0005 1 1 0 0 0 0 0

Detailed information about the deployment options for FLeet is available here.

Usage

The following contains information about each module of FLeet along with instructions for implementing a new application.

Server

Performs the descent computation and updates the model

Edit SPUpdater.java and SPSampler.java
Set profiling method in MasterOrchestrator.java

Driver

Initializes the Server and performs a periodic evaluation

Edit Driver.java

Android Client

Performs the gradient computation

Edit SPGradientGenerator.java

Existing applications

cppNN

Image classification application based on Convolutional Neural Networks and written in C++.

Enable/disable compression
- Enable compressed SGD: #define DISTILLATION_MODE 1
- Disable compressed SGD: #define DISTILLATION_MODE 0
- Files: Server cppNN_backend.cpp, Driver cppNN_backend.cpp, Client cppNN-lib.cpp, network.h

MLP

Image classification application based on multilayer perceptron and written in Java.

LR

Facebook check-in prediction application based on logistic regression and written in Java.

DL4J

Port of DL4J library on FLeet.

Datasets

MNIST

See Quick Start.

EMNIST

Download Python dependencies:
- Sklearn
- Scipy
- Numpy
python emnistParser.py --help
For parsing into FLeet change to the DL4J app:
- See all TODO notes in the Driver.
- Change the application in the Server and Client (see Usage).
- Give the output of the parser as input dataset for FLeet.

CIFAR

Download CIFAR-10 from here or CIFAR-100 from here and extract.

For CIFAR-100:

cd cifar-100-binary/
mv train.bin data_batch_1.bin
mv test.bin test_batch.bin

For parsing into FLeet:
- Comment out these lines and comment in these lines. Choose this line for CIFAR-100 or this line for CIFAR-10.
- Comment out these lines and comment in these lines for CIFAR-100 or these lines for CIFAR-10.
- Comment in this line for both datasets.

AgatheMead/fleet