SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Authors: Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we confirm our theoretical results on simulated and real datasets (extended MNIST by Cohen et al. (2017)). |
| Researcher Affiliation | Collaboration | 1EPFL, Lausanne 2Based on work performed at Google Research, New York. 3Google Research, New York 4Courant Institute, New York. |
| Pseudocode | Yes | Algorithm 1 SCAFFOLD: Stochastic Controlled Averaging for federated learning |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | Our real world experiments run logistic regression (convex) and 2 layer fully connected network (non-convex) on the EMNIST (Cohen et al., 2017). |
| Dataset Splits | No | The paper describes how data is distributed among clients for heterogeneity, but it does not provide explicit details about training, validation, or test dataset splits (e.g., percentages or methodology). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We always use global step-size ηg = 1 and tune the local step-size ηl individually for each algorithm. ... 1 epoch for local update methods corresponds to 5 local steps (0.2 batch size), and 20% of clients are sampled each round. We fix µ = 1 for FEDPROX... |