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..
Distributionally Robust Federated Averaging
Authors: Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give corroborating experimental evidence for our theoretical results in federated learning settings. |
| Researcher Affiliation | Academia | Yuyang Deng Mohammad Mahdi Kamani Mehrdad Mahdavi The Pennsylvania State University EMAIL |
| Pseudocode | Yes | Algorithm 1: Distributionally Robust Federated Averaging (DRFA) |
| Open Source Code | Yes | The code repository used for these experiments can be found at: https://github.com/MLOPTPSU/TorchFed/ |
| Open Datasets | Yes | We use three datasets, namely, Fashion MNIST [48], Adult [1], and Shakespeare [4] datasets. |
| Dataset Splits | No | The paper mentions using 'test accuracies' and 'training' but does not provide specific details on train/validation/test dataset splits, percentages, or methodology for partitioning the data. |
| Hardware Specification | Yes | We implement our algorithm based on Distributed API of Py Torch [41] using MPI as our main communication interface, and on an Intel Xeon E5-2695 CPU with 28 cores. |
| Software Dependencies | No | We implement our algorithm based on Distributed API of Py Torch [41] using MPI as our main communication interface (PyTorch is mentioned but its specific version number is not provided, nor are specific versions for MPI or Python). |
| Experiment Setup | Yes | We use different synchronization gaps of τ {5, 10, 15}, and set η = 0.1 and γ = 8 10 3. [...] The batch size is 50 and synchronization gap is τ = 10. We set η = 0.1 for all algorithms, γ = 8 10 3 for DRFA and AFL, and q = 0.2 for q-Fed Avg. |