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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Budgeted Online Model Selection and Fine-Tuning via Federated Learning
Authors: Pouya M. Ghari, Yanning Shen
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real datasets demonstrate the effectiveness of the proposed algorithm. Experiments on regression and image classification datasets showcase the effectiveness of OFMS-FT compared with state-of-the-art alternatives. |
| Researcher Affiliation | Academia | Pouya M. Ghari EMAIL Department of Electrical Engineering and Computer Science Center for Pervasive Communications and Computing University of California Irvine; Yanning Shen EMAIL Department of Electrical Engineering and Computer Science Center for Pervasive Communications and Computing University of California Irvine |
| Pseudocode | Yes | Algorithm 1 OFMS-FT: Online Federated Model Selection and Fine-Tuning; Algorithm 2 Cluster Generation by Client i at Learning Round t |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to a code repository. |
| Open Datasets | Yes | Image classification tasks are performed over CIFAR-10 (Krizhevsky, 2009) and MNIST (Lecun et al., 1998) datasets. Online regression tasks are performed on Air (Zhang et al., 2017) and WEC (Neshat et al., 2018) datasets. |
| Dataset Splits | Yes | For CIFAR-10, each client receives 155 testing data samples from one class and 5 samples from each of the other nine classes. In the case of MNIST, each client receives at least 133 samples from one class and at least 5 samples from the other classes. The 200 testing data samples are randomly shuffled and are sequentially presented to each client over T = 200 learning rounds. |
| Hardware Specification | Yes | All experiments were carried out using Intel(R) Core(TM) i7-10510U CPU @ 1.80 GHz 2.30 GHz processor with a 64-bit Windows operating system. |
| Software Dependencies | No | The paper mentions software components like ReLU activation, SGD, and VGG architecture, but it does not provide specific version numbers for any libraries or frameworks used in the implementation. |
| Experiment Setup | Yes | The learning rates ηi for all methods are set to be 10/T where T = 200. Furthermore, the fine-tuning learning rate is set to ηf = 10^-3 / T. In the experiments, using Fed-OMD and Per Fed Avg, each client performs one epoch of stochastic gradient descent (SGD) with learning rate of 0.001 on its batch of data to fine-tune the model. |