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..
Multimodal Federated Learning via Contrastive Representation Ensemble
Authors: Qiying Yu, Yang Liu, Yimu Wang, Ke Xu, Jingjing Liu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough evaluations and ablation studies on image-text retrieval and VQA tasks showcase the superiority of Cream FL over state-of-the-art FL methods. |
| Researcher Affiliation | Academia | Qiying Yu1,4, Yang Liu1,4 , Yimu Wang2, Ke Xu3, Jingjing Liu1 1 Institute for AI Industry Research, Tsinghua University 2 University of Waterloo 3 Carnegie Mellon University 4 Shanghai Artificial Intelligence Laboratory EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Cream FL algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code. |
| Open Datasets | Yes | We randomly choose a subset of MS-COCO (Lin et al., 2014) with 50,000 image-text pairs as public dataset. ... We distribute Flicker30K (Plummer et al., 2015) to 15 multimodal clients, CIFAR100 (Krizhevsky et al., 2009) to 10 uni-modal image clients, and AGNEWS (Zhang et al., 2015) to 10 uni-modal text clients... |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly specify a validation dataset split or how it's used for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory used for experiments. |
| Software Dependencies | No | The paper specifies models used (e.g., ResNet-101, BERT) and an optimizer (AdamP) but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We choose Res Net-101 (He et al., 2016) and Res Net-18 as the server and client image models, respectively, and BERT (base) (Devlin et al., 2018) and GRU (Chung et al., 2014) as the text models. The representation dimension d is 512 for both image and text. We use Adam P optimizer with initial learning rate 0.0002 and cosine learning rate scheduler for server model. |