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 through Local Memorization
Authors: Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on a suite of federated datasets that this approach achieves significantly higher accuracy and fairness than state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1Inria, Universit e Cหote d Azur, Sophia Antipolis, France 2Accenture Labs, Sophia Antipolis, France. |
| Pseudocode | Yes | Algorithm 1 k NN-Per (Typical usage) Learn global model using available clients with Fed Avg. for each client m [M] (in parallel) do Build datastore using Sm. At inference on x X, return hm,ฮปm (x) given by (7) end for |
| Open Source Code | Yes | Code is available at https: //github.com/omarfoq/knn-per. |
| Open Datasets | Yes | We evaluate k NN-Per on four federated datasets spanning a wide range of machine learning tasks: language modeling (Shakespeare (Caldas et al., 2018; Mc Mahan et al., 2017)), image classification (CIFAR-10 and CIFAR100 (Krizhevsky, 2009)), handwritten character recognition (FEMNIST (Caldas et al., 2018)). |
| Dataset Splits | Yes | For FEMNIST and Shakespeare, we randomly split each local dataset into training (60%), validation (20%), and test (20%) sets. For CIFAR-10 and CIFAR-100, we maintained the original training/test data split and used 20% of the training dataset as validation dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'FAISS library' and 'Proto NN' but does not specify their version numbers or any other software dependencies with versions. |
| Experiment Setup | Yes | In all experiments with CIFAR-10 and CIFAR-100, training spanned 200 rounds with full clients participation at each round for all methods. The learning rate was reduced by a factor 10 at round 100 and then again at round 150. For Shakespeare, 10% of clients were sampled uniformly at random without replacement at each round, and we trained for 300 rounds with a constant learning rate. For FEMNIST, 5% of the clients participated at each round for a total 1000 rounds, with the learning rate dropping by a factor 10 at round 500 and 750. |