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..
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
Authors: Irene Wang, Prashant Nair, Divya Mahajan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate FLu ID using five real-world mobile clients. The evaluations show that Invariant Dropout maintains baseline model efficiency while alleviating the performance bottleneck of stragglers through a dynamic and lightweight runtime approach. |
| Researcher Affiliation | Collaboration | Irene Wang1,3, Prashant J. Nair1, Divya Mahajan2,3 University of British Columbia1, Microsoft2, Georgia Institute of Technology3 |
| Pseudocode | Yes | Algorithm 1 outlines the FLu ID framework. |
| Open Source Code | Yes | Source code is available at https://github.com/iwang05/FLu ID |
| Open Datasets | Yes | The FEMNIST datasets consist of images of numbers and letters, partitioned based on the writer of the character in non-IID setting. [...] The Shakespeare dataset partitions data based on roles in Shakespeares plays in non-IID setting. [...] The CIFAR10 dataset consists of images, partitioned using the same strategy as Fj ORD [HLA+21] and the IID partition provided by the Flower [BTM+20]. |
| Dataset Splits | No | The paper mentions partitioning strategies for datasets (non-IID, IID) and refers to prior work for partitioning. However, it does not provide specific train/validation/test split percentages, sample counts, or explicit details about how the validation set was created or used for model selection/hyperparameter tuning, beyond general mentions of accuracy evaluation during training. |
| Hardware Specification | Yes | Table 1: Software-Hardware specifications of clients Device Year Android Version CPU (Cores) LG Velvet 5G 2020 10 1 2.4 GHz Kryo 475 Prime + 1 2.2 GHz Kryo 475 Gold + 6 1.8 GHz Kryo 475 Silver Google Pixel 3 2018 9 4 2.5 GHz Kryo 385 Gold + 4 1.6 GHz Kryo 385 Silver Samsung Galaxy S9 2018 10 4 2.8 GHz Kryo 385 Gold + 4 1.7 GHz Kryo 385 Silver Samsung Galaxy S10 2019 11 2 2.73 GHz Mongoose M4 + 2 2.31 GHz Cortex-A75 + 4 1.95 GHz Cortex-A55 Google Pixel 4 2019 12 1 2.84 GHz Kryo 485 + 3 2.42 GHz Kryo 485 + 4 1.78 GHz Kryo 485 |
| Software Dependencies | Yes | FLu ID is implemented on top of the Flower (v0.18.0) [BTM+20] framework and Tensor Flow Lite [Goo] from Tensor Flow v2.8.0 [ABC+16]. |
| Experiment Setup | Yes | We trained the models for 100, 250, and 65 epochs for CIFAR10, FEMNIST, and Shakespeare datasets, respectively. [...] For this example, we choose thresholds of 180%, 10%, and 500%, respectively, for these three datasets and compute their invariant neurons. [...] Table 3 presents the percentage of invariant neurons observed at different threshold values, and the overall training accuracy of the FEMNIST model, using a sub-model size of 0.75 for the stragglers. [...] Table 4 presents the accuracy when stragglers are assigned to 4 equal-sized clusters (sub-model size 0.65, 0.75, 0.85, 0.95). [...] The accuracy results of varying the straggler ratios while using 0.75-sized sub-models are summarized in Figure 8. [...] We scaled FLu ID to 1,000 clients with the FEMNIST dataset for 500 global training rounds. We run with a client sampling ratio of 10%... |