Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Authors: Laurent Condat, Artavazd Maranjyan, Peter Richtarik
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our proposed method Lo Co DL and compare it with several other methods that also allow for CC and converge linearly to x . We also include Grad Skip (Maranjyan et al., 2022) and Scaffold (Mc Mahan et al., 2017) in our comparisons. We focus on a regularized logistic regression problem, which has the form (1) with ... We show the results with the a5a , diabetes , w1a datasets in Figures 1, 2, 3, respectively. |
| Researcher Affiliation | Academia | Laurent Condat, Artavazd Maranjyan & Peter Richtárik Computer Science Program, CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal, 23955-6900, Kingdom of Saudi Arabia & SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence (SDAIA-KAUST AI) EMAIL |
| Pseudocode | Yes | Algorithm 1 Lo Co DL |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the Lo Co DL method, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We considered several datasets from the Lib SVM library (Chang & Lin, 2011) (3-clause BSD license). We show the results with the a5a , diabetes , w1a datasets in Figures 1, 2, 3, respectively. ... Finally, we also run experiments on MNIST dataset (Le Cun et al., 1998) in Figure 6. |
| Dataset Splits | No | We prepared each dataset by first shuffling it, then distributing it equally among the n clients (since m in (11) is an integer, the remaining datapoints were discarded). This describes how data was distributed among clients but does not specify explicit training, validation, or test splits for evaluating the model. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | Yes | We focus on a regularized logistic regression problem, which has the form (1) with ... µ is the regularization parameter, set so that κ = 104. ... For all algorithms, we used the theoretical parameter values given in their available convergence results (Corollary 3.2 for Lo Co DL). We tried to tune the parameter values, such as k in rand-k and the (average) number of local steps per round, but this only gave minor improvements. For instance, ADIANA in Figure 1 was a bit faster with the best value of k = 20 than with k = 30. Increasing the learning rate γ led to inconsistent results, with sometimes divergence. |