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..
Accelerated Federated Learning with Decoupled Adaptive Optimization
Authors: Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on real federated tasks and datasets demonstrates the superior performance of our momentum decoupling adaptive optimization model against several state-of-the-art regular federated learning and federated optimization approaches. |
| Researcher Affiliation | Collaboration | 1 Auburn University, USA 2 Sony AI, Japan 3 Baidu Research, China 4 University of Oregon, USA. |
| Pseudocode | Yes | Algorithm 1 Fed DA+SGDM; Algorithm 2 Fed DA+ADAM& Fed DA+Ada Grad |
| Open Source Code | No | We promise to release our open-source codes on Git Hub and maintain a project website with detailed documentation for long-term access by other researchers and end-users after the paper is accepted. |
| Open Datasets | Yes | Datasets. We focus on three popular computer vision and natural language processing tasks over three representative benchmark datasets respectively: (1) image classification over CIFAR-100 (Krizhevsky, 2009). ... (2) image classification over EMNIST (Hsieh et al., 2020). ... and (3) text classification over Stack Overflow (Tensor Flow, 2019). |
| Dataset Splits | No | The paper describes how data is partitioned among clients and how hyperparameters are tuned based on training loss, but it does not specify explicit training/validation/test splits (e.g., 80/10/10) for the datasets used. It also mentions validation data for hyperparameter tuning is 'often inaccessible in federated settings'. |
| Hardware Specification | Yes | Our experiments were conducted on a compute server running on Red Hat Enterprise Linux 7.2 with 2 CPUs of Intel Xeon E5-2650 v4 (at 2.66 GHz) and 8 GPUs of NVIDIA Ge Force GTX 2080 Ti (with 11GB of GDDR6 on a 352-bit memory bus and memory bandwidth in the neighborhood of 620GB/s), 256GB of RAM, and 1TB of HDD. |
| Software Dependencies | No | All the codes were implemented based on the Tensorflow Federated (TFF) package (Ingerman & Ostrowski, 2019). |
| Experiment Setup | Yes | Unless otherwise explicitly stated, we used the following default parameter settings in the experiments, as shown in Table 12. |