# CoCoL: A Communication Efficient Decentralized Collaborative Method for Multi-Robot Systems

## TL;DR

CoCoL is a decentralized collaborative learning method for multi-robot systems that significantly reduces communication costs and handles data heterogeneity, achieving high accuracy in complex tasks.

## Contribution

It introduces a novel mirror descent-based framework with approximate Newton updates and gradient tracking for efficient, robust multi-robot learning.

## Key findings

- Reduces communication rounds and bandwidth consumption
- Maintains state-of-the-art accuracy in heterogeneous data scenarios
- Effective in non-IID, streaming, and dynamic network conditions

## Abstract

Collaborative learning enhances the performance and adaptability of multi-robot systems in complex tasks but faces significant challenges due to high communication overhead and data heterogeneity inherent in multi-robot tasks. To this end, we propose CoCoL, a Communication efficient decentralized Collaborative Learning method tailored for multi-robot systems with heterogeneous local datasets. Leveraging a mirror descent framework, CoCoL achieves remarkable communication efficiency with approximate Newton-type updates by capturing the similarity between objective functions of robots, and reduces computational costs through inexact sub-problem solutions. Furthermore, the integration of a gradient tracking scheme ensures its robustness against data heterogeneity. Experimental results on three representative multi robot collaborative learning tasks show the superiority of the proposed CoCoL in significantly reducing both the number of communication rounds and total bandwidth consumption while maintaining state-of-the-art accuracy. These benefits are particularly evident in challenging scenarios involving non-IID (non-independent and identically distributed) data distribution, streaming data, and time-varying network topologies.