Bayesian Nonparametric Federated Learning of Neural Networks
Authors: Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaeni
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We thoroughly evaluate the proposed model and demonstrate its utility empirically in Section 4. |
| Researcher Affiliation | Collaboration | 1IBM Research, Cambridge 2MIT-IBM Watson AI Lab 3Center for Computational Health. Correspondence to: Mikhail Yurochkin <mikhail.yurochkin@ibm.com>. |
| Pseudocode | Yes | Algorithm 1 Single Layer Neural Matching |
| Open Source Code | Yes | Code is available at https://github.com/IBM/ probabilistic-federated-neural-matching |
| Open Datasets | Yes | To verify our methodology we simulate federated learning scenarios using two standard datasets: MNIST and CIFAR10. |
| Dataset Splits | No | The paper mentions partitioning datasets into 'J batches' for federated learning simulations but does not specify global training/validation/test splits or a separate validation set for model selection, only 'Test Accuracy'. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In this and subsequent experiments each local neural network has Lj = 100 hidden neurons. We compare PFNM, using the communication procedure from Section 3.3 (σ = σ0 = γ0 = 1 across experiments) to federated averaging and the distributed optimization approach, downpour SGD (D-SGD) of Dean et al. (2012). Further, k-Means requires us to choose k, which we set to K = min(500, 50J). |