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..
Decentralized Learning: Theoretical Optimality and Practical Improvements
Authors: Yucheng Lu, Christopher De Sa
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we compare De TAG with other decentralized algorithms on multiple vision benchmarks, including CIFAR10/100 and Image Net. We substantiate our theory and show De TAG converges faster on unshuffled data and in sparse networks. Furthermore, we study a De TAG variant, De TAG*, that practically speeds up data-center-scale model training. |
| Researcher Affiliation | Academia | Yucheng Lu EMAIL Department of Computer Science Cornell University Ithaca, NY 14850, USA Christopher De Sa EMAIL Department of Computer Science Cornell University Ithaca, NY 14850, USA |
| Pseudocode | Yes | Algorithm 1 Decentralized Stochastic Gradient Descent with Factorized Consensus Matrices (De Facto) on worker i Algorithm 2 Decentralized Stochastic Gradient Tracking with By-Phase Accelerated Gossip (De TAG) on worker i Algorithm 3 Accelerated Gossip (AG) with R steps Algorithm 4 De TAG with Momentum Acceleration (De TAGM) on worker i |
| Open Source Code | No | No explicit statement about code release or links to repositories for the methodology described in this paper were found. |
| Open Datasets | Yes | Empirically, we compare De TAG with other decentralized algorithms on multiple vision benchmarks, including CIFAR10/100 and Image Net. |
| Dataset Splits | Yes | To create the decentralized data, we first sort all the data points based on its labels, shuffle the first X% data points and then evenly split to different workers. The X controls the degree of decentralization, we test X = 0, 25, 50, 100 and plot the results in Figure 2. |
| Hardware Specification | No | We use 8-GPU ring graph there and uses each GPU as an individual worker. Subsequently, in Subsection 7.2, we extend the system to a 32-GPU ring graph and train Resnet18 on Image Net. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | In the experiment of training Le Net on CIFAR10, we tune the step size using grid search inside the following range: {5e-3, 1e-3, 5e-4, 2.5e-4, 1e-4, 5e-5}. ... For De TAG, we further tune the accelerated gossip parameter η within {0, 0.1, 0.2, 0.4} and phase length R within {1, 2, 3}. We fix the momentum term to be 0.9 and weight decay to be 1e-4. ... The hyperparameters adopted for each runs are shown in Table 3 and Table 4. |