flsim_dqn

CSCE689 Deep Reinforcement Learning Course Project (Fall 2022)

[Slides] [Project Report] [5-min YouTube Presentation]

In this project, we implemented the D-DQN model for device selection during Federated Learning process and reproduced some results in the following paper.

Hao Wang, Zakhary Kaplan, Di Niu, Baochun Li. "Optimizing Federated Learning on Non-IID Data with Reinforcement Learning," in the Proceedings of IEEE INFOCOM, Beijing, China, April 27-30, 2020.

To install env:

conda env create -f environment_torch_cpu.yml

The Double DQN server for learning how to select devices are implemented in /server/dqn.py.

Evaluation done in report:

1. Reproduce Fig 1 (done by Tian)

   • python run.py --config=configs/MNIST/mnist_fedavg_iid.json
   • python run.py --config=configs/MNIST/mnist_fedavg_noniid.json
   • python run.py --config=configs/MNIST/mnist_kcenter_noniid.json
   • python run.py --config=configs/MNIST/mnist_kmeans_noniid.json
   • until model achieves 99% test accuracy
   • python plots/plot_fig_1.py

2. Reproduce Figure 3, PCA on clients weights (by Tian and YuTing)

   • 100 clients with 2 PCA components
   • 20 clients with 2 PCA components
   • python plots/plot_fig_3.py

3. Reproduce Fig 5(a), D-DQN trained on MNIST dataset (by Tian and YuTing)

   • select 10 out of 100, each client has 600 data
     • python run.py --config=dqn_noniid_10_100.json
   • select 4 out of 20, each client has 3000 data
     • python run.py --config=dqn_noniid_4_20.json
   • Plot Total_reward vs. Training Episodes (YuTing)

4. Compare using the target reward function vs. new proposed difference function (Tian, NiuCheng)

   • select 10 out of 100, each client has 600 data, using the new reward function
     • python run.py --config=dqn_noniid_10_100_difference.json
     • python run.py --config=dqn_noniid_4_20_difference.json
   • Plot Total_reward vs. Training Episodes (YuTing)
     • check plots/ folder

5. Reproduce Fig 6(c) on MNIST datasets with non-IID degree of 0.8 (Tian)

   • For each of two settings, compare DQN_infer vs. FedAvg (Random selection) vs. K-Center vs. K-means
   • Plot Testing Accuracy vs. Communication Rounds (YuTing)
   • check plots/ folder

Reference: FL-Lottery

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About

Welcome to FLSim, a PyTorch based federated learning simulation framework, created for experimental research in a paper accepted by IEEE INFOCOM 2020:

Hao Wang, Zakhary Kaplan, Di Niu, Baochun Li. "Optimizing Federated Learning on Non-IID Data with Reinforcement Learning," in the Proceedings of IEEE INFOCOM, Beijing, China, April 27-30, 2020.

Installation

To install FLSim, all that needs to be done is clone this repository to the desired directory.

Dependencies

FLSim uses Anaconda to manage Python and it's dependencies, listed in environment.yml. To install the fl-py37 Python environment, set up Anaconda (or Miniconda), then download the environment dependencies with:

conda env create -f environment.yml

Usage

Before using the repository, make sure to activate the fl-py37 environment with:

conda activate fl-py37

Simulation

To start a simulation, run run.py from the repository's root directory:

python run.py
  --config=config.json
  --log=INFO

run.py flags

• --config (-c): path to the configuration file to be used.
• --log (-l): level of logging info to be written to console, defaults to INFO.

config.json files

FLSim uses a JSON file to manage the configuration parameters for a federated learning simulation. Provided in the repository is a generic template and three preconfigured simulation files for the CIFAR-10, FashionMNIST, and MNIST datasets.

For a detailed list of configuration options, see the wiki page.

If you have any questions, please feel free to contact Hao Wang (haowang@ece.utoronto.ca)