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 [1].
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them
Authors: Saber Salehkaleybar, Arsalan Sharifnassab, S. Jamaloddin Golestani
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experiments We evaluated the performance of MRE-C on two learning tasks and compared with the averaging method (AVGM) in (Zhang et al., 2012). ... In Fig. 3, the average of θˆ − θ is computed over 100 instances for the different number of machines in the range [104, 106]. Both experiments suggest that the average error of MRE-C keep decreasing as the number of machines increases. |
| Researcher Affiliation | Academia | Saber Salehkaleybar EMAIL Arsalan Sharifnassab EMAIL S. Jamaloddin Golestani EMAIL Department of Electrical Engineering Sharif University of Technology Tehran, Iran |
| Pseudocode | Yes | Algorithm 1: MRE-C algorithm // Constructing each sub-signal at machine i 1 obtain θi according to (10). 2 s the closest point in grid G to θi. ... |
| Open Source Code | Yes | The source code of MRE-C algorithm is publicly available at https: //github.com/sabersalehk/MRE_C. |
| Open Datasets | No | The paper describes how synthetic data was generated for the experiments (e.g., "each sample (X, Y ) is generated based on a linear model Y = XT θ +E, where X, E, and θ are sampled from N(0, Id d), N(0, 0.01), and uniform distribution over [0, 1]d, respectively."), but does not specify the use of any publicly available or open datasets with concrete access information. |
| Dataset Splits | No | The paper mentions experimental parameters like "d = 2" and "n = 1" (samples per machine) and that results are "computed over 100 instances". However, it does not provide specific details regarding train/test/validation dataset splits, percentages, or methodology for partitioning the data. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory configurations used for running the experiments. |
| Software Dependencies | No | The paper does not specify any ancillary software dependencies (e.g., libraries, frameworks, or solvers) with their version numbers that would be needed to replicate the experiments. |
| Experiment Setup | Yes | In both experiments, we consider a two dimensional domain (d = 2) and assumed that each machine has access to one sample (n = 1). In Fig. 3, the average of θˆ − θ is computed over 100 instances for the different number of machines in the range [104, 106]. ... We consider square loss function with l2 norm regularization: f(θ) = (XT θ Y )2 + 0.1 θ 2. |