Federated Learning for Collaborative Maze Discovery

Maze Discovery using Multiple Robots via Federated Learning - ISCC2024

This work explores the application of federated learning (FL) in a maze discovery scenario using robots equipped with LiDAR sensors. The objective is to train classification models capable of identifying grid area shapes within two distinct square mazes, each featuring irregularly shaped walls. A key challenge arises from the unique wall shapes in each maze, which prevent a model trained on one maze from generalizing to the other. To overcome this, FL enables robots exploring a single maze to collaboratively share and aggregate knowledge, enhancing their ability to accurately classify shapes in the unseen maze. This use case highlights the potential of FL in real-world applications, demonstrating its ability to improve classification accuracy and robustness in complex, dynamic tasks such as maze discovery.

Relevant Resources

Paper: https://ieeexplore.ieee.org/abstract/document/10733625 or https://arxiv.org/abs/2407.01596

Github repository: https://github.com/ICONgroupCWC/MapDiscoveryDemo

FL implementation: https://oulurepo.oulu.fi/handle/10024/50678