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..
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
Authors: Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our framework with a variety of datasets, models, and types of non-IIDness. Our results demonstrate that FEDCOLLAB effectively mitigates negative transfer across a wide range of FL algorithms and consistently outperforms other clustered FL algorithms. |
| Researcher Affiliation | Academia | 1University of Illinois Urbana-Champaign, Champaign, IL, USA. |
| Pseudocode | Yes | Algorithm 1 Training client discriminator |
| Open Source Code | Yes | Our code is available at https://github.com/baowenxuan/Fed Collab. |
| Open Datasets | Yes | We evaluate our framework on three models and datasets: we train a 3-layer MLP for Fashion MNIST (Xiao et al., 2017), a 5-layer CNN for CIFAR-10 (Krizhevsky, 2009), and an Image Net pre-trained Res Net-18 (He et al., 2016) for CIFAR-100 (with 20 coarse labels). |
| Dataset Splits | No | Table 4 shows 'training / testing samples' but does not explicitly provide validation splits for the main model training. Algorithm 1 mentions 'Train-valid split' for the client discriminator, but not for the primary FL model training. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We train a 3-layer MLP for Fashion MNIST (Xiao et al., 2017), a 5-layer CNN for CIFAR-10 (Krizhevsky, 2009), and an Image Net pre-trained Res Net-18 (He et al., 2016) for CIFAR-100 (with 20 coarse labels)... For all three datasets, we use a light-weight two-layer MLP as the client discriminator to estimate pairwise distribution distances... We run C = 6, 8, 10 on all three settings, and report the best result. Finally, we choose C = 10 for MNIST and CIFAR-10 experiments, and C = 8 for CIFAR-100 experiments. |