Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
Authors: Bikang Pan, Wei Huang, Ye Shi
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These theoretical claims have been further supported by empirical experiments. Our code is available at: https://github.com/Pan Bikang/Prompt Folio.git. ... In this section, we conduct experiments with the CLIP model to empirically demonstrate the performance advantages of Prompt Folio. |
| Researcher Affiliation | Academia | Bikang Pan Shanghai Tech University panbk2023@shanghaitech.edu.cn Wei Huang RIKEN Center for Advanced Intelligence Project wei.huang.vr@riken.jp Ye Shi Shanghai Tech University shiye@shanghaitech.edu.cn |
| Pseudocode | Yes | Algorithm 1 (Prompt Folio) Global-Local Prompt Portfolio |
| Open Source Code | Yes | Our code is available at: https://github.com/Pan Bikang/Prompt Folio.git. |
| Open Datasets | Yes | The experiment is conducted on the CIFAR-100 dataset by default, ... We use CIFAR-100 [25], Domain Net [36], Office-Caltech10 [15], Oxford Pets [35], and DTD [11] |
| Dataset Splits | No | The paper mentions using standard datasets like CIFAR-100 but does not explicitly state the train/validation/test splits, their percentages, or sample counts, nor does it explicitly reference predefined splits. |
| Hardware Specification | Yes | The experiments are conducted on a cluster with 2 Intel Xeon 5218R, 512GB memory, and 8 NVIDIA Tesla A40 GPUs 48GB. |
| Software Dependencies | No | The paper does not provide specific version numbers for key software components such as programming languages, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | The experiment is conducted on the CIFAR-100 dataset by default, with the model trained for 10 epochs locally and the results evaluated over 100 communication rounds. |