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 using Hypernetworks
Authors: Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test p Fed HN empirically in several personalized federated learning challenges and find that it outperforms previous methods. Finally, since hypernetworks share information across clients, we show that p Fed HN can generalize better to new clients whose distributions differ from any client observed during training. |
| Researcher Affiliation | Collaboration | 1Bar-Ilan University, Ramat Gan, Israel 2Nvidia, Tel-Aviv, Israel. Correspondence to: Aviv Shamsian <EMAIL>, Aviv Navon <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Personalized Federated Hypernetwork |
| Open Source Code | Yes | We make our source code publicly available at: https: //github.com/Aviv Sham/p Fed HN. |
| Open Datasets | Yes | We evaluate p Fed HN in several learning setups using three common image-classification datasets: CIFAR10, CIFAR100, and Omniglot (Krizhevsky & Hinton, 2009; Lake et al., 2015). |
| Dataset Splits | Yes | For the CIFAR experiments, we pre-allocate 10, 000 training examples for validation. For the Omniglot dataset, we use a 70%/15%/15% split for train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments, only mentioning general experimental setup. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We tune the hyperparameters of all methods using a pre-allocated held-out validation set. Full experimental details are provided in Appendix B. ... For all experiments presented in the main text, we use a fully-connected hypernetwork with 3 hidden layers of 100 hidden units each. For all relevant baselines, we aggregate over 5 clients at each round. We set K = 3 ,i.e., 60 local steps, for the p Fed Me algorithm... |