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..
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
Authors: Jiaqi Wang, Xingyi Yang, Suhan Cui, Liwei Che, Lingjuan Lyu, Dongkuan (DK) Xu, Fenglong Ma
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that p Fed HR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, p Fed HR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner. |
| Researcher Affiliation | Collaboration | Jiaqi Wang1 Xingyi Yang2 Suhan Cui1 Liwei Che1 Lingjuan Lyu3 Dongkuan Xu4 Fenglong Ma1 1The Pennsylvania State University 2National University of Singapore 3Sony AI 4North Carolina State University |
| Pseudocode | Yes | Algorithm 1: Reassembly Candidate Search [...] Algorithm 1: Algorithm Flow of p Fed HR. |
| Open Source Code | Yes | Source code can be found in the link https://github.com/Jackqq Wang/pfed HR |
| Open Datasets | Yes | Datasets. We conduct experiments for the image classification task on MNIST, SVHN, and CIFAR10 datasets under both IID and non-IID data distribution settings, respectively. We split the datasets into 80% for training and 20% for testing. |
| Dataset Splits | No | The paper mentions 'We split the datasets into 80% for training and 20% for testing' but does not specify a separate validation split or how validation was performed if it was implicitly part of training. |
| Hardware Specification | Yes | The proposed p Fed HR is implemented in Pytorch 2.0.1 and runs on NVIDIA A100 with CUDA version 12.0 on a Ubuntu 20.04.6 LTS server. |
| Software Dependencies | Yes | The proposed p Fed HR is implemented in Pytorch 2.0.1 and runs on NVIDIA A100 with CUDA version 12.0 on a Ubuntu 20.04.6 LTS server. |
| Experiment Setup | Yes | The hyperparameter λ in Eq. (6) is 0.2. We use Adam as the optimizer. The learning rate of the local client learning and the server fine-tuning learning rate are both equal to 0.001. |