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

arXiv: https://arxiv.org/abs/2508.20898 · HTML: https://mlsanity.com/paper/2508.20898/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [NEXT: In-Network Nonconvex Optimization](https://mlsanity.com/paper/1602.00591/) (2016)
- [Distributed Stochastic Gradient Tracking Methods](https://mlsanity.com/paper/1805.11454/) (2018)
- [AIDE: Fast and Communication Efficient Distributed Optimization](https://mlsanity.com/paper/1608.06879/) (2016)
- [BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression](https://mlsanity.com/paper/2201.13320/) (2022)
- [Decentralized Gradient Tracking with Local Steps](https://mlsanity.com/paper/2301.01313/) (2023)
- [CP-SLAM: Collaborative Neural Point-based SLAM System](https://mlsanity.com/paper/2311.08013/) (2023)
- [Multi-Agent Distributed Optimization via Inexact Consensus ADMM](https://mlsanity.com/paper/1402.6065/) (2014)
- [On the Performance of Gradient Tracking with Local Updates](https://mlsanity.com/paper/2210.04757/) (2022)
- [DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning](https://mlsanity.com/paper/2109.08665/) (2021)
- [Robust and Communication-Efficient Collaborative Learning](https://mlsanity.com/paper/1907.10595/) (2019)
- [Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions](https://mlsanity.com/paper/2110.02181/) (2021)
- [Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.02179/) (2022)
- [The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity](https://mlsanity.com/paper/2403.15654/) (2024)
- [MAexp: A Generic Platform for RL-based Multi-Agent Exploration](https://mlsanity.com/paper/2404.12824/) (2024)
- [MDPGT: Momentum-based Decentralized Policy Gradient Tracking](https://mlsanity.com/paper/2112.02813/) (2021)
- [Distributed Optimization Methods for Multi-Robot Systems: Part I -- A Tutorial](https://mlsanity.com/paper/2301.11313/) (2023)
- [A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization](https://mlsanity.com/paper/1809.01212/) (2018)
- [Distributed Quasi-Newton Method for Multi-Agent Optimization](https://mlsanity.com/paper/2402.06778/) (2024)

## Abstract (arXiv)

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.