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..
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
| 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. |