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.
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Authors: Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 20,104 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,717 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 | 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... |