PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
Authors: Benyuan Sun, Hongxing Huo, YI YANG, Bo Bai
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of Partial Fed, on real-world non i.i.d. tasks including cross-domain classification and detection. |
| Researcher Affiliation | Industry | Benyuan Sun Hongxing Huo Yi Yang Bo Bai Media Technology Lab, Huawei {sunbenyuan,huohongxing1,yangyi16,baibo3}@huawei.com |
| Pseudocode | Yes | Algorithm 1 Partial Fed: Partially-Loaded Federated Learning and Algorithm 2 Partial Fed-Adaptive |
| Open Source Code | No | The paper mentions using Py Torch [21] and Detectron2 [34] with their respective licenses, but does not provide a link or statement for the open-sourcing of the authors' own implementation code for Partial Fed. |
| Open Datasets | Yes | For federated classification, we use the popular Office-Home [30] dataset... For the detection task, we adopt challenging multi-domain detection dataset UODB [33] |
| Dataset Splits | No | Table 5 (UODB description) lists 'Train' and 'Test' data sizes for each dataset, but no explicit validation split or percentages are provided for any dataset, nor for Office-Home. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned for the experimental setup. |
| Software Dependencies | No | The paper mentions 'Py Torch [21]' and 'Detectron2 [34]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The temperature parameter τt in Equ. 5 is initialized as 5.0 and annealed to 0 as in [27]. The overall learning scheme is summarized in Alg. 2. For details, we list the hyper parameters in the Appendix. |