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..
pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
Authors: Jiaqi Wang, Qi Li, Lingjuan Lyu, Fenglong Ma
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments across three datasets, examining both IID and non-IID settings. The results demonstrate that p Fed Club outperforms baseline approaches, achieving state-of-the-art performance. |
| Researcher Affiliation | Collaboration | Jiaqi Wang1 Qi Li2 Lingjuan Lyu3 Fenglong Ma1 1The Pennsylvania State University 2Iowa State University 3Sony AI |
| Pseudocode | Yes | Algorithm 1: The CMSR Algorithm |
| Open Source Code | Yes | The source code can be found at https://github.com/Jackqq Wang/24club. |
| Open Datasets | Yes | In our experiments, we utilize three commonly used datasets to validate the performance of the proposed p Fed Club, including MNIST4, SVHN5, and CIFAR-106. 4https://yann.lecun.com/exdb/mnist/ 5http://ufldl.stanford.edu/housenumbers/ 6https://www.cs.toronto.edu/~kriz/cifar.html |
| Dataset Splits | Yes | We randomly divide the datasets into three parts: 72% for training, 20% for testing, and 8% as the public dataset. |
| Hardware Specification | Yes | We run all the experiments on NVIDIA A100 with CUDA version 12.0 on a Ubuntu 20.04.6 LTS server. |
| Software Dependencies | Yes | All baselines and the proposed p Fed Club are implemented in Pytorch 2.0.1. |
| Experiment Setup | Yes | For the proposed p Fed Club and baseline p Fed HR, we set the number of clusters K = 4 following [23], and the local training epoch and the server finetuning epoch are equal to 10 and 3, respectively. The hyperparameter λ in Eq. (5) is 0.2. The hyperparameter τ in Eq. (3) is 0.07. We use Adam as the optimizer. The learning rate of the local client learning and the server fine-tuning learning rate equal 0.001. |