Breaking the centralized barrier for cross-device federated learning
Authors: Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U. Stich, Ananda Theertha Suresh
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also perform a thorough experimental exploration of MIME s performance on real world datasets (implemented here). We report the results of thorough experimental analysis demonstrating that both MIME and MIMELITE indeed converge faster than FEDAVG. |
| Researcher Affiliation | Collaboration | Sai Praneeth Karimireddy EPFL sai.karimireddy@epfl.ch Martin Jaggi EPFL martin.jaggi@epfl.ch Satyen Kale Google Research satyenkale@google.com Mehryar Mohri Google Research mohri@google.com Sashank J. Reddi Google Research sashank@google.com Sebastian U. Stich EPFL sebastian.stich@epfl.ch Ananda Theertha Suresh Google Research theertha@google.com |
| Pseudocode | Yes | Algorithm 1 Mime and Mime Lite |
| Open Source Code | No | The paper mentions using TensorFlow Federated [60] and cites other frameworks like Fed JAX [52, 53], but it does not provide an explicit statement or link to the open-source code for the specific methodology (MIME) presented in this paper. |
| Open Datasets | Yes | We run five simulations on three real-world federated datasets: EMNIST62 with i) a linear classifier, ii) an MLP, and iii) a CNN, iv) a char RNN on Shakespeare, and v) an LSTM for next word prediction on Stack Overflow, all accessed through Tensorflow Federated [60]. |
| Dataset Splits | No | The paper uses 'Validation % accuracies' in Table 2 but does not explicitly provide specific details on the train/validation/test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory specifications, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions 'TensorFlow Federated [60]' as a tool used, but it does not provide specific version numbers for TensorFlow Federated or any other software dependencies needed to replicate the experiments. |
| Experiment Setup | Yes | The learning rates were individually tuned and other optimizer hyper-parameters such as β for momentum, β1, β2, ε0 for Adam and Ada Grad were left to their default values, unless explicitly stated otherwise. We train a 2 hidden layer (300µ-100) MLP on EMNIST62 with 10 local epochs for 1k rounds. |