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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
Authors: Seungjoo Lee, Thanh-Long V. Le, Jaemin Shin, Sung-Ju Lee
NeurIPS 2024 | Venue PDF | 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 EMAIL |
| 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) |