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..
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
Authors: Shiqiang Wang, Mingyue Ji
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results also verify the advantage of Fed AU over baseline methods with various participation patterns. |
| Researcher Affiliation | Collaboration | Shiqiang Wang IBM T. J. Watson Research Center Yorktown Heights, NY 10598 EMAIL Mingyue Ji Department of ECE, University of Utah Salt Lake City, UT 84112 EMAIL |
| Pseudocode | Yes | Algorithm 1: Fed Avg with pluggable aggregation weights |
| Open Source Code | Yes | The code for reproducing our experiments is available via the following link: https://shiqiang.wang/code/fedau |
| Open Datasets | Yes | We consider four image classification tasks, with datasets including SVHN (Netzer et al., 2011), CIFAR-10 (Krizhevsky & Hinton, 2009), CIFAR-100 (Krizhevsky & Hinton, 2009), and CINIC-10 (Darlow et al., 2018) |
| Dataset Splits | No | The paper mentions training and test data, but does not specify the exact split percentages or a dedicated validation set split for reproduction. |
| Hardware Specification | Yes | The experiments were split between a desktop machine with RTX 3070 GPU and an internal GPU cluster. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The grid for the local step size γ is {10 2, 10 1.75, 10 1.5, 10 1.25, 10 1, 10 0.75, 10 0.5} and the grid for the global step size η is {100, 100.25, 100.5, 100.75, 101, 101.25, 101.5}. To reduce the complexity of the search, we first search for the value of γ with η = 1, and then search for η while fixing γ to the value found in the first search. We consider the training loss at 500 rounds for determining the best γ and η. |