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..
Anarchic Federated Learning
Authors: Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the proposed algorithms with extensive experiments on real-world datasets. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA; 2Department of Statistics, Iowa State University, Ames, IA 50011, USA; 3Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. |
| Pseudocode | Yes | The general framework of AFL is illustrated in Algorithm 1. [...] Algorithm 2 AFA-CD Algorithm for Cross-Device AFL. [...] Algorithm 3 The AFA-CS Algorithm for Cross-Silo AFL. |
| Open Source Code | No | No concrete statement about releasing open-source code for the methodology or a link to a code repository is provided in the paper. |
| Open Datasets | Yes | We use i) logistic regression (LR) on manually partitioned non-i.i.d. MNIST dataset (Le Cun et al., 1998), ii) convolutional neural network (CNN) for manually partitioned CIFAR-10 (Krizhevsky, 2009), and iii) recurrent neural network (RNN) on natural non-i.i.d. dataset Shakespeare (Mc Mahan et al., 2016). |
| Dataset Splits | No | For MNIST and CIFAR-10, each dataset has ten classes of images. To impose statistical heterogeneity, we split the data based on the classes (p) of images each worker contains. We distribute the data to M = 10(or 100) workers such that each worker contains only certain classes with the same number of training/test samples. This describes a data partitioning strategy for heterogeneity and mentions training/test samples, but it does not specify explicit reproducible train/validation/test splits (e.g., 80/10/10% or absolute counts) for the overall dataset. |
| Hardware Specification | No | No specific hardware (GPU models, CPU models, cluster specs) used for running the experiments is detailed in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) are explicitly mentioned in the paper. |
| Experiment Setup | Yes | For MNIST and CIFAR-10, we use global learning rate η = 1.0 and local learning rate ηL = 0.1. For MNIST, the batch size is 64 and the total communication round is 150. For CIFAR-10, the batch size is 500 and the total communication round is 10000. For the Shakespeare dataset, the global learning rate is η = 50, the local learning rate is ηL = 0.8, batch size is b = 10, and the total communication round is 300. |