On Sample Optimality in Personalized Collaborative and Federated Learning
Authors: Mathieu Even, Laurent Massoulié, Kevin Scaman
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically illustrate our theory in Appendix A on synthetic datasets, with clustered agents (as in this section), as well as in a setting where agents are distributed according to a more general distribution of agent . |
| Researcher Affiliation | Collaboration | 1Inria Paris Département d informatique de l ENS, PSL Research University 2Microsoft-Inria Joint Center |
| Pseudocode | Yes | Algorithm 1 All-for-all algorithm |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology described in this paper or a direct link to a code repository was found. |
| Open Datasets | Yes | for the MNIST dataset, deff is less than 3, while the ambient dimension is 712 [22]. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was found. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment were provided. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, optimizer settings, or detailed training configurations for the algorithms. |