Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home Environments

Overview

This is a PyTorch-based code implementation for the paper titled Federated Learning of Socially Appropriate Agent Behaviors in Simulated Home Environments paper published at the HRI '24: Workshop on Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI), ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2024. The paper explores the use of Federated Learning (FL) and Federated Continual Learning (FCL) techniques to train agents in simulated home environments, focusing on socially appropriate behaviors.

Table of Contents

• Installation
• Data
• Training
• Results
• Citation
• Acknowledgement

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Installation

Ensure you have the necessary dependencies installed. Run the following command to set up the environment:

pip install -r requirements.txt

Dataset

Access to the Manners-DB data files can be requested here: https://github.com/jonastjoms/MANNERS-DB/. The csv file with the labels, once acquired, should be placed under Data/all_data.csv. Currently, a dummy file is included for reference.

Training

The repository is divided into two parts

• Federated Learning
• Federated Continual Learning

Federated Learning

Each Strategy is setup in a separate Jupyter Notebook (FedAvg, FedBN, FedProx, FedOptAdam, FedDistil) both without and with data augmentation.

Federated Continual Learning

FCL strategies are implemented as python packages. To execute the code for the benchmark on MANNERS-DB run the following:

bash Federated Continual Learning/run_FCL_local.sh

Results

Results are included in the Results as two different sections, Federated Learning and Federated Continual Learning.

Citation

@INPROCEEDINGS{Checker2024Federated,  
  author		= {S. {Checker} and N. {Churamani} and H. {Gunes}},  
  booktitle		= {{Workshop on Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI), 16th ACM/IEEE International Conference on Human-Robot Interaction (HRI)}},
  title			= {{Federated Learning of Socially Appropriate Agent Behaviors in Simulated Home Environments}},   
  year			= {2024},  
 }

Acknowledgement

Funding: S. Checker contributed to this work while undertaking a remote visiting studentship at the Department of Computer Science and Technology, University of Cambridge. N. Churamani and H. Gunes are supported by Google under the GIG Funding Scheme.

Open Access: For open access purposes, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

Data Access Statement: This study involves secondary analyses of the existing datasets, that are described and cited in the text.