Federated Multi-Task Learning under a Mixture of Distributions
Authors: Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, Richard Vidal
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on FL benchmarks show that our approach provides models with higher accuracy and fairness than state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1Inria, Université Côte d Azur, France, {othmane.marfoq, giovanni.neglia}@inria.fr 2Inria, Université de Lille, France, aurelien.bellet@inria.fr 3Accenture Labs, France, {richard.vidal, laetitia.kameni}@accenture.com |
| Pseudocode | Yes | Algorithm 1: Fed EM (see also the more detailed Alg. 2 in App. D.1) |
| Open Source Code | Yes | Code is available at https://github.com/omarfoq/Fed EM. |
| Open Datasets | Yes | We evaluated our method on five federated benchmark datasets spanning a wide range of machine learning tasks: image classification (CIFAR10 and CIFAR100 [33]), handwritten character recognition (EMNIST [8] and FEMNIST [7]),5 and language modeling (Shakespeare [7, 47]). |
| Dataset Splits | Yes | For all tasks, we randomly split each local dataset into training (60%), validation (20%) and test (20%) sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Mobile Net-v2 [55]' and 'Stacked-LSTM [25]' as models but does not specify software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | For each method and each task, the learning rate and the other hyperparameters were tuned via grid search (details in App. I.2). Fed Avg+ updated the local model through a single pass on the local dataset. Unless otherwise stated, the number of components considered by Fed EM was M = 3, training occurred over 80 communication rounds for Shakespeare and 200 rounds for all other datasets. At each round, clients train for one epoch. |