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

• URL: http://arxiv.org/abs/2508.20898v1
• Date: Thu, 28 Aug 2025 15:25:48 GMT
• Title: CoCoL: A Communication Efficient Decentralized Collaborative Method for Multi-Robot Systems
• Authors: Jiaxi Huang, Yan Huang, Yixian Zhao, Wenchao Meng, Jinming Xu,
• Abstract summary: We propose a Communication efficient decentralized Collaborative Learning method tailored for multi-robot systems with heterogeneous local datasets.<n>CoCoL achieves remarkable communication efficiency with approximate Newton-type updates by capturing the similarity between objective functions of robots.
• Score: 12.818331958107429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks [57.95170323315603]
  We introduce CollaPipe, a distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving networks.<n>In CollaPipe, the encoder part is adaptively partitioned into variable-sized segments and deployed across mobile devices for pipeline-parallel training, while the decoder is deployed on edge servers to handle generative tasks.<n>To enhance training efficiency, we formulate a joint optimization problem that adaptively allocates model segments, micro-batches, bandwidth, and transmission power.
  arXiv  Detail & Related papers  (2025-09-24T07:54:01Z)
• KAITIAN: A Unified Communication Framework for Enabling Efficient Collaboration Across Heterogeneous Accelerators in Embodied AI Systems [5.241889216655924]
  KAITIAN is a novel distributed communication framework for AI workloads.<n>It integrates vendor-optimized communication libraries for intra-group efficiency with general-purpose communication protocols for inter-group interoperability.<n>It can accelerate training time by up to 42% compared to baseline homogeneous systems.
  arXiv  Detail & Related papers  (2025-05-15T11:29:43Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.<n>Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach [27.86468387141422]
  Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity.<n>Current FEEL algorithms struggle with non-independent and non-identically distributed (non-IID) data, leading to elevated communication costs and compromised model accuracy.<n>We introduce a clustered data sharing framework, mitigating data heterogeneity by selectively sharing partial data from cluster heads to trusted associates.<n>Experiments show that the proposed framework facilitates FEEL on non-IID datasets with faster convergence rate and higher model accuracy in a limited communication environment.
  arXiv  Detail & Related papers  (2024-06-14T07:22:39Z)
• Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining [119.89373423433804]
  Area Under Precision-Recall (AUPRC) was introduced as an effective metric. Serverless multi-party collaborative training can cut down the communications cost by avoiding the server node bottleneck. We propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC.
  arXiv  Detail & Related papers  (2023-08-06T06:51:32Z)
• Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration [2.958532752589616]
  Communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots.
  arXiv  Detail & Related papers  (2023-05-26T16:56:00Z)
• Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs [59.96266198512243]
  We propose a decentralized and federated learning algorithm for tasks that are positively and negatively correlated. Our algorithm uses gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other. We conduct experiments on a synthetic Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset.
  arXiv  Detail & Related papers  (2022-12-21T18:58:24Z)
• Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks [82.02891936174221]
  Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach. In this paper, a novel semantic-aware CDRL method is proposed to enable a group of untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
  arXiv  Detail & Related papers  (2021-11-23T18:24:47Z)
• Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis [8.863089484787835]
  We propose a distributed approximate Newton-type Newton-type training scheme, namely FedOVA, to solve the heterogeneous statistical challenge brought by heterogeneous data. FedOVA decomposes a multi-class classification problem into more straightforward binary classification problems and then combines their respective outputs using ensemble learning.
  arXiv  Detail & Related papers  (2021-10-14T17:35:24Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning [49.15799302636519]
  We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers) In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place.
  arXiv  Detail & Related papers  (2020-09-29T13:08:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.