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..
Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness
Authors: Shiyun Lin, Yuze Han, Xiang Li, Zhihua Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct a large number of experiments to show the empirical superiority of our method over several state-of-the-art methods on the three aspects. |
| Researcher Affiliation | Academia | Shiyun Lin1,2 Yuze Han2 Xiang Li2 Zhihua Zhang1,2 1Center for Statistical Science, Peking University 2School of Mathematical Sciences, Peking University EMAIL EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 lp-proj: Projection-based Lp Regularized Personalized Federated Learning |
| Open Source Code | Yes | Source code for the reproduction of numerical results is available at https://github.com/desternylin/perfed. |
| Open Datasets | Yes | We test lp-proj as well as other comparable algorithms on six datasets from common ML and FL benchmarks [50, 8]. |
| Dataset Splits | Yes | For each client, the training and testing data are pre-specified as in the ML community, and 20% of training data is randomly extracted to construct a validation set, keeping the remaining 80% as the training set. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA RTX 3090 GPU. |
| Software Dependencies | Yes | The experiments are implemented with Python 3.8.13 and PyTorch 1.11.0. |
| Experiment Setup | Yes | More details about hyperparameter tuning are provided in Appendix C.2. |