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..
Federated Learning under Partially Disjoint Data via Manifold Reshaping
Authors: Ziqing Fan, Jiangchao Yao, Ruipeng Zhang, Lingjuan Lyu, Yanfeng Wang, Ya Zhang
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on a range of datasets to demonstrate that our Fed MR achieves much higher accuracy and better communication efficiency. |
| Researcher Affiliation | Collaboration | Ziqing Fan, Jiangchao Yao B, Ruipeng Zhang EMAIL Cooperative Medianet Innovation Center, Shanghai Jiao Tong University Shanghai AI Laboratory Lingjuan Lyu EMAIL Sony AI Ya Zhang, Yanfeng Wang B EMAIL Cooperative Medianet Innovation Center, Shanghai Jiao Tong University Shanghai AI Laboratory |
| Pseudocode | Yes | Algorithm 1 Fed MR Input: a set of K clients that participate in each round, the initial model weights w0, the maximal round T, the learning rate η, the local training epochs E. |
| Open Source Code | Yes | Source code is available at: https://github.com/MediaBrain-SJTU/FedMR. |
| Open Datasets | Yes | We adopt four popular benchmark datasets SVHN (Netzer et al. (2011)), FMNIST (Xiao et al. (2017)), CIFAR10 and CIFAR100 (Le Cun et al. (1998)) in federated learning and a real-world PCDD medical dataset ISIC2019 (Codella et al. (2018); Tschandl et al. (2018); Combalia et al. (2019)) to conduct experiments. |
| Dataset Splits | Yes | In order to better study pure PCDD, for the former four benchmarks, we split each dataset into ϱ clients, each with ς categories, abbreviated as PϱCς. For example, P10C10 in CIFAR100 means that we split CIFAR100 into 10 clients, each with 10 classes. |
| Hardware Specification | No | The paper discusses hardware in a hypothetical context of local clients being mobile phones or other small devices, but does not specify any hardware used for the experiments themselves. |
| Software Dependencies | No | The paper mentions model architectures (Res Net18, wide Res Net, Efficient Net) and an optimizer (SGD), but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The optimizer is SGD with a learning rate 0.01, the weight decay 10 5 and momentum 0.9. The batch size is set to 128 and the local updates are set to 10 epochs for all approaches. |