(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
Authors: Seungjoo Lee, Thanh-Long V. Le, Jaemin Shin, Sung-Ju Lee
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on three benchmark datasets demonstrate that our approach significantly improves performance and bridges the gap with SSL, particularly in scenarios with scarce labeled data. |
| Researcher Affiliation | Academia | Seungjoo Lee Thanh-Long V. Le Jaemin Shin Sung-Ju Lee KAIST Republic of Korea {seungjoo.lee,thanhlong0780,jaemin.shin,profsj}@kaist.ac.kr |
| Pseudocode | Yes | A Algorithm Algorithm 1 (FL)2: Few-Labels Federated semi-supervised Learning |
| Open Source Code | Yes | The source code is available at https://github.com/seungjoo-ai/FLFL-Neur IPS24 |
| Open Datasets | Yes | Data setup We evaluate (FL)2 in three public datasets: CIFAR10, CIFAR100 [12], and SVHN [32]. |
| Dataset Splits | No | The paper describes data distribution (IID/non-IID) and the number of labeled/unlabeled samples, but does not explicitly provide percentages or counts for train/validation/test splits. |
| Hardware Specification | Yes | We used RTX3090 GPUs throughout the experiment. |
| Software Dependencies | No | The paper mentions 'official Py Torch implementation' and 'Git Hub repository for Semi FL', but does not specify version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | In our experiments, we use 100 clients, with a participation ratio of 0.1 per communication round (K = 10)...Both the server and clients optimize their local datasets for five local epochs, with 800 communication rounds. We employ the momentum SGD optimizer with a learning rate of 0.03, momentum of 0.9, and weight decay of 5e-4...the perturbation strength ρ is set to 0.1 for the CIFAR10 and SVHN datasets and 1.0 for the CIFAR100 dataset. (Section 5, Learning setup) and Table 4: Hyperparameters in our experiments Method Fed Match [7] Fed Con [9] Semi FL [8] (FL)2 (Appendix C) |