Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Federated Learning with Partial Model Personalization
Authors: Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, Lin Xiao
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 45,322 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,533 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on realworld image, text, and speech datasets demonstrate that (a) partial personalization can obtain most of the benefits of full model personalization with a small fraction of personal parameters, and, (b) the alternating update algorithm outperforms the simultaneous update algorithm by a small but consistent margin. |
| Researcher Affiliation | Collaboration | 1Paul G. Allen School of Computer Science & Engineering, University of Washington 2Meta AI. |
| Pseudocode | Yes | Algorithm 1 Fed Alt / Fed Sim |
| Open Source Code | Yes | The code to reproduce the experimental results is publicly available.1 https://github.com/krishnap25/FL_ partial_personalization |
| Open Datasets | Yes | We use the Stack Overflow dataset, where each device corresponds to the questions and answers of one user on stackoverflow.com. ... We use GLDv2 (Weyand et al., 2020), a large-scale image dataset... We use the EMNIST dataset (Cohen et al., 2017)... We construct a federated version of the Libri Speech dataset (Panayotov et al., 2015)... |
| Dataset Splits | Yes | All the tuning of hyperparameters was performed on validation data, formed by holding out 20% of the training data on each device. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow Federated' and 'Py Torch' but does not provide specific version numbers for these or any other key software components. |
| Experiment Setup | Yes | Table 9. Hyperparameters for each dataset/task. ... All the tuning of hyperparameters was performed on validation data, formed by holding out 20% of the training data on each device. |