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 [1].
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. |