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 | Archive PDF | Plain Text | 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. |