Software Benchmark Artifact

Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning (ComDML)

Seyed Mahmoud Sajjadi Mohammadabadi
Open Source Artifact • 2024 Reproducibility Suite | Cited in IEEE ICDCS 2024

Abstract

Production-ready decentralized multi-agent training benchmark implementing gossip-based neighbor synchronization, load rebalancing, and communication-constrained consensus protocols. Cited in IEEE ICDCS 2024.

BibTeX Citation

@misc{sajjadi2024comdml,
  title        = {Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning ({ComDML})},
  author       = {Sajjadi Mohammadabadi, Seyed Mahmoud},
  year         = {2024},
  howpublished = {\url{https://github.com/mahmoudsajjadi/ComDML}}
}

Author Profile

🧠

Seyed Mahmoud Sajjadi Mohammadabadi, Ph.D.

AI Systems Researcher • University of Nevada, Reno • Connected Future Labs

Google Scholar (430+ Citations) • LinkedIn • GitHub (@mahmoudsajjadi) • ORCID

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