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..
FedWon: Triumphing Multi-domain Federated Learning Without Normalization
Authors: Weiming Zhuang, Lingjuan Lyu
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experimentation on five datasets and five models, our comprehensive experimental results demonstrate that Fed Won surpasses both Fed Avg and the current state-of-the-art method (Fed BN) across all experimental setups, achieving notable accuracy improvements of more than 10% in certain domains. |
| Researcher Affiliation | Industry | Weiming Zhuang Sony AI EMAIL Lingjuan Lyu Sony AI EMAIL |
| Pseudocode | Yes | Listing 1 provides the implementation of WSConv in Py Torch. |
| Open Source Code | No | The source code will be released. |
| Open Datasets | Yes | We conduct experiments for multi-domain FL using three datasets: Digits-Five (Li et al., 2021), Office-Caltech-10 (Gong et al., 2012), and Domain Net (Peng et al., 2019). Digits-Five consists of five sets of 28x28 digit images, including MNIST (Le Cun et al., 1998), SVHN (Netzer et al., 2011), USPS (Hull, 1994), Synth Digits (Ganin & Lempitsky, 2015), MNIST-M (Ganin & Lempitsky, 2015); |
| Dataset Splits | No | The paper does not explicitly provide details about a validation dataset split (percentages or absolute counts) separate from training and test sets. |
| Hardware Specification | Yes | We implement Fed Won using Py Torch (Paszke et al., 2017) and run experiments on a cluster of eight NVIDIA T4 GPUs. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al., 2017)" but does not specify a version number or provide version numbers for other key software libraries or dependencies. |
| Experiment Setup | Yes | We use cross-entropy loss and stochastic gradient optimization (SGD) as the optimizer with learning rates tuned over the range of [0.001, 0.1] for all methods. |