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DOI

FFL - Fast Federated Learning

Fast Federated Learning (FFL) is a C/C++-based Federated Learning framework built on top of the parallel programming FastFlow framework. It exploits the Cereal library to efficiently serialise the updates sent over the network and the libtorch library to fully bypass the need for Python code. It has been successfully tested on x86_64, ARM and RISC-V platforms. FFL comes with scripts for automatically installing the framework and reproducing all the experiments reported in the original paper.

Getting started

To setup the whole system and to compile the example just run:

source setup.sh

This script will automatically download all the required libraries, update the environment variables, build the dff_run utility, launch CMake and build all the available examples.

Reproducibility

Starting from a fresh Ubuntu 22.04 installation, the first step to reproduce the results reported in the paper is to update the available packages information and install the build-essential, cmake, libopencv-dev, and unzip packages:

sudo apt-get update
sudo apt-get install build-essential cmake libopencv-dev unzip

Then, clone the FFL repository locally and build the code as follows (the setup.sh script will take care of everything, as mentioned in the previous section):

git clone https://github.com/alpha-unito/FastFederatedLearning.git
cd FastFederatedLearning
source setup.sh

Lasty, all that is needed to run the full set of experiments is to run the reproduce.sh script:

bash reproduce.sh

This script will take care of running in a replicate manner (5 times) all available examples (3) in all the available configurations (3), for a total of 5*3*3=45 runs. The mean execution time for each combination will be reported on the output, and logs will be saved for each experiment. Inside the reproduce script, the MAX_ITER variable can be set to change the replica factor of the experiments.

Power measurements

The power measurements on the x86_64 and ARM platforma has been done trhough the Powermon utility. This software has to be started in parallel with the code execution on another shell, and then killed right after the desired computation is concluded.

On the RISC-V platform, on the other hand, we used a National Instruments oscilloscope directly connected to the hardware board. This has been done due to the lack of other software-based solution and the high precision of the available machinery.

Available examples

Six different examples are currently available, obtained as the combination of three different communication topologies (master-worker, peer-to-peer, tree-based) and two execution modalities (shared-memory, distributed). The executables have the following names:

Topology Shared-memory Distributed
Master-worker masterworker masterworker_dist
Peer-to-peer p2p p2p_dist
Tree-based edgeinference edgeinference_dist

Shared-memory examples

The shared-memory examples (masterworker, p2p, and edgeinference) are straght-forward to run. They can be executed with the following syntax:

./masterworker     [forcecpu=0/1] [rounds=10] [epochs/round=2]   [data_path="../../data"] [num_workers=3]
./p2p [forcecpu=0/1] [rounds=10] [epochs/round=2]   [data_path="../../data"] [num_peers=3]
./edgeinference   [forcecpu=0/1] [groups=3]  [clients/groups=1] [model_path] [data_path]

where forcecpu indicates if to force the CPU use (1) or to allow the GPU use (0), rounds indicate the number of federated rounds to perform, epochs/round the number of training epoch to be run for each round, data_path is the path to the data files, num_workers/num_peers/groups is the number of instances to create, client/groups is the number of clients in each group, and model_path is the path of the torchscript model to be used.

Distributed examples

The distributed examples require an additional file to run correctly: a json distributed configuration file specifying on which host each FastFlow instance will run and which is his role (especially for the master-worker scenario). A generic distrubted configuration file for masterworker_dist looks like this:

{
   "groups" : [
      {   
         "preCmd" : "MKL_NUM_THREADS=4 OMP_NUM_THREADS=4 taskset -c 0-3",
         "endpoint" : "localhost:8000",
         "name" : "Federator"
      },
      {   
         "preCmd" : "MKL_NUM_THREADS=4 OMP_NUM_THREADS=4 taskset -c 20-23",
         "endpoint" : "128.0.0.1:8001",
         "name" : "W0"
      },
      {   
         "endpoint" : "host2:8022",
         "name" : "W1"
      },
      {   
         "endpoint" : "134.342.12.12:6004",
         "name" : "W2"
      }
   ]
}

where preCmd indicates commands to be appended before the training command, endpoint indicates the host and port where the FastFlow instance will be created, and name specifies the role of the node:

Name Meaning Use case
Federator Server master-worker
W[N] Client/Peer[rank] master-worker/peer-to-peer
G[N] Group[rank] (0 means root) tree-based

Once the distributed configuration file is available, then running the examples is simalar to the shared-memory scenario, but additionally requires the dff_run utility:

dff_run -V -p TCP -f [distributed_config_file] ./masterworker_dist [forcecpu=0/1] [rounds=10] [epochs/round=2] [data_path="../../data"] [num_workers=3]
dff_run -V -p TCP -f [distributed_config_file] ./p2p_dist [forcecpu=0/1] [rounds=10] [epochs/round=2] [data_path="../../data"] [num_peers=3]
dff_run -V -p TCP -f [distributed_config_file] ./edgeinference [forcecpu=0/1] [groups=3] [clients/groups=1] [model_path] [data_path]

where -V allow to visualise all the clients' output from the launching console, -p TCP forces the use of the TCP backend (MPI is also available), and -f [distributed_config_file] requires the distributed configuration file path.

Required libraries

The whole project in wrote in C/C++ and to be compiled require a version of CMake > 3.0 and a C++17 compatible C/C++ compiler.

Furthermore, the following software libraries are needed:

Library Version Link
FastFlow DistributedFF GitHub
Cereal 1.3.2 GitHub
libtorch 2.0.0 Webpage
OpenCV 4.6.0 Webpage

Publications

This work has been published at the 2023 edition of the ACM Computing Frontiers conference.

Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medic, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, and Marco Aldinucci. “Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning.” In: Proceedings of the 20th ACM International Conference on Computing Frontiers, CF 2023, Bologna, Italy, May 9-11, 2023. Ed. by Andrea Bartolini, Kristian F. D. Rietveld, Catherine D. Schuman, and Jose Moreira. Bologna, Italy: Acm, May 2023, pp. 73– 83. isbn: 979-8-4007-0140-5/23/05. doi: 10.1145/3587135.3592211. url: https://doi.org/10.1145/3587135.3592211

Contacts

This repository is maintained by Gianluca Mittone ([email protected]).