Distributed Event-Based Learning via ADMM
<|reference_start|>Distributed Event-Based Learning via ADMM: We consider a distributed learning problem, where agents minimize a global objective function by exchanging information over a network. Our approach has two distinct features: (i) It substantially reduces communication by triggering communication only when necessary, and (ii) it is agnostic to the data-distribution among the different agents. We can therefore guarantee convergence even if the local data-distributions of the agents are arbitrarily distinct. We analyze the convergence rate of the algorithm and derive accelerated convergence rates in a convex setting. We also characterize the effect of communication drops and demonstrate that our algorithm is robust to communication failures. The article concludes by presenting numerical results from a distributed LASSO problem, and distributed learning tasks on MNIST and CIFAR-10 datasets. The experiments underline communication savings of 50% or more due to the event-based communication strategy, show resilience towards heterogeneous data-distributions, and highlight that our approach outperforms common baselines such as FedAvg, FedProx, and FedADMM.<|reference_end|>
@article{er2024distributed,
    title={Distributed Event-Based Learning via ADMM},
    author={Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach},
    journal={arXiv preprint arXiv:2405.10618},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10618},
    primaryClass={cs.LG math.OC stat.ML}
}