Optimizing the Collaboration Structure in Cross-Silo Federated Learning
Authors: Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |