Federated Minimax Optimization with Client Heterogeneity
Authors: Pranay Sharma, Rohan Panda, Gauri Joshi
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results support our theoretical claims. In this section, we evaluate the empirical performance of the proposed algorithms. We consider a robust neural training problem ... and a fair classification problem. Figures such as Figure 3, Figure 4, and Figure 5 show 'Test Accuracy' over 'Number of Communications' for various settings, demonstrating empirical evaluation. |
| Researcher Affiliation | Academia | Pranay Sharma EMAIL Department of Electrical and Computer Engineering Carnegie Mellon University; Rohan Panda EMAIL Department of Electrical and Computer Engineering Carnegie Mellon University; Gauri Joshi EMAIL Department of Electrical and Computer Engineering Carnegie Mellon University. All authors are affiliated with Carnegie Mellon University, which is an academic institution, and use .edu email addresses. |
| Pseudocode | Yes | Algorithm 1 Fed-Norm-SGDA and Fed-Norm-SGDA+ |
| Open Source Code | No | The paper does not contain any explicit statements about releasing their source code, nor does it provide a link to a code repository. It mentions 'implemented using parallel training tools in Py Torch 1.0.0 and Python 3.6.3' but this refers to third-party tools, not their own implementation. |
| Open Datasets | Yes | We consider a robust neural training problem ... on CIFAR10 dataset, with VGG11 model. ... Fair Classification ... on the CIFAR10 dataset, with the VGG11 model. CIFAR10 is a widely recognized public dataset. |
| Dataset Splits | No | The paper describes data heterogeneity across clients using Dirichlet distribution and discusses client sampling for local epochs, as well as partial client participation levels (e.g., P=5, P=10, FCP n=15). However, it does not specify the explicit train/test/validation splits for the datasets (e.g., CIFAR10) used in the experiments. |
| Hardware Specification | Yes | Our experiments were run on a network of n = 15 clients, each equipped with an NVIDIA Titan X GPU. |
| Software Dependencies | Yes | Our algorithm was implemented using parallel training tools in Py Torch 1.0.0 and Python 3.6.3. |
| Experiment Setup | Yes | For both robust NN Training and fair classification experiments, we use batch-size of 32 in all the algorithms. Momentum parameter 0.9 is used only in Momentum Local SGDA(+). ... Table 3: Parameter values for experiments in robust NN training experiments. ... Table 4: Parameter values for experiments in fair classification experiments. These tables specify Client Learning Rate (ηc y), Client Learning Rate (ηc x), and Server Learning Rate (γs x = γs y) values. Clients sample the number of epochs they run locally via uniform sampling over the set {2 . . . , E}, i.e., τi Unif[2 : E]. |