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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Personalized Federated Learning with Moreau Envelopes
Authors: Canh T. Dinh, Nguyen Tran, Josh Nguyen
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate the performance of pFedMe using both real and synthetic datasets that capture the statistical diversity of clients data. We show that pFedMe outperforms the vanilla FedAvg and a meta-learning based personalized FL algorithm Per-FedAvg in terms of convergence rate and local accuracy. |
| Researcher Affiliation | Academia | 1The University of Sydney, Australia EMAIL, EMAIL 2The University of Melbourne, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1 pFedMe: Personalized Federated Learning using Moreau Envelope Algorithm |
| Open Source Code | Yes | The code and datasets are available online1. 1https://github.com/Charlie Dinh/pFedMe |
| Open Datasets | Yes | We consider a classification problem using both real (MNIST) and synthetic datasets. MNIST [51] is a handwritten digit dataset containing 10 labels and 70,000 instances. |
| Dataset Splits | Yes | All datasets are split randomly with 75% and 25% for training and testing, respectively. |
| Hardware Specification | No | The paper mentions that |
| Software Dependencies | Yes | All experiments were conducted using PyTorch [52] version 1.4.0. |
| Experiment Setup | Yes | We fix the subset of clients S = 5 for MNIST, and S = 10 for Synthetic. We compare the algorithms using both cases of the same and fine-tuned learning rates, batch sizes, and number of local and global iterations. ... We fix |D| = 20, R = 20, K = 5, and T = 800 for MNIST, and T = 600 for Synthetic, β = 2 for pFedMe (ˆα and ˆβ are learning rates of Per-FedAvg). |