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..
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
Authors: Benyuan Sun, Hongxing Huo, YI YANG, Bo Bai
NeurIPS 2021 | Venue PDF | 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 EMAIL |
| 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. |