FedBABU: Toward Enhanced Representation for Federated Image Classification
Authors: Jaehoon Oh, SangMook Kim, Se-Young Yun
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show consistent performance improvements and an efficient personalization of Fed BABU. Table 1 describes the accuracy of Fed Avg on CIFAR100 according to different FL settings (f, τ, and s) with 100 clients. |
| Researcher Affiliation | Academia | Jaehoon Oh Graduate School of KSE, KAIST jhoon.oh@kaist.ac.kr Sangmook Kim , Se-Young Yun Graduate School of AI, KAIST {sangmook.kim, yunseyoung}@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 Training procedure of Fed BABU. |
| Open Source Code | Yes | The code is available at https://github.com/jhoon-oh/Fed BABU. |
| Open Datasets | Yes | We used two public datasets, the CIFAR (Krizhevsky et al., 2009) and EMNIST (Cohen et al., 2017) , for performance evaluation. |
| Dataset Splits | Yes | We set the number of clients to 100 and then each client has 500 training data and 100 test data; the classes in the training and test data sets are the same. |
| Hardware Specification | Yes | For computation costs, we used a single TITAN RTX and the entire training time of Fed BABU on CIFAR100 using Mobile Net takes 2 hours when f=1.0 and τ=1. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' but does not specify a version number for PyTorch or other software dependencies. |
| Experiment Setup | Yes | We set the number of clients to 100 and then each client has 500 training data and 100 test data; the classes in the training and test data sets are the same. We control FL environments with three hyperparameters: client fraction ratio f, local epochs τ, and shards per user s. ... The learning rate starts with 0.1 and is decayed by a factor of 0.1 at half and three-quarters of total updates. ... fine-tuning with the fine-tuning epochs of τf; Here, one epoch is equal to 10 updates in our case because each client has 500 training samples and the batch size is 50. |