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..
Federated Accelerated Stochastic Gradient Descent
Authors: Honglin Yuan, Tengyu Ma
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify the efficiency of FEDAC in Section 5. Numerical results suggest a considerable improvement of FEDAC over all three baselines, namely FEDAVG, (distributed) Minibatch-SGD, and (distributed) Accelerated Minibatch-SGD [Dekel et al., 2012, Cotter et al., 2011], especially in the regime of highly infrequent communication and abundant workers. In this section, we validate our theory and demonstrate the efficiency of FEDAC via experiments. |
| Researcher Affiliation | Academia | Honglin Yuan Stanford University EMAIL Tengyu Ma Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Federated Accelerated Stochastic Gradient Descent (FEDAC) |
| Open Source Code | Yes | Code repository link: https://github.com/hongliny/Fed Ac-Neur IPS20. |
| Open Datasets | Yes | on 2-regularized logistic regression for UCI a9a dataset [Dua and Graff, 2017] from Lib SVM [Chang and Lin, 2011]. |
| Dataset Splits | No | The paper mentions using the UCI a9a dataset but does not specify training, validation, or test splits (e.g., percentages, sample counts, or explicit standard split references) within the text. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., specific CPU/GPU models, cloud instances, or memory specifications) used for running the experiments. It refers generally to "distributed computing resources" and "abundant workers". |
| Software Dependencies | No | The paper mentions using data from "Lib SVM [Chang and Lin, 2011]" but does not provide specific version numbers for any software dependencies or libraries (e.g., Python, PyTorch, TensorFlow, etc.) used to implement and run the experiments. |
| Experiment Setup | Yes | The regularization strength is set as 10 3. The hyperparameters (γ, , β) of FEDAC follows FEDAC-I where strong-convexity µ is chosen as regularization strength 10 3. We test the settings of M = 22, . . . , 213 workers and K = 20, . . . , 28 synchronization interval. For all four algorithms, we tune the learning-rate only from the same set of levels within [10 3, 10]. |