HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
Authors: Momin Ahmad Khan, Yasra Chandio, Fatima Anwar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation show that HYDRA-FL significantly boosts accuracy over Fed NTD and MOON in attack settings while maintaining performance in benign settings. |
| Researcher Affiliation | Academia | Momin Ahmad Khan University of Massachusetts, Amherst makhan@umass.edu Yasra Chandio University of Massachusetts, Amherst ychandio@umass.edu Fatima Muhammad Anwar University of Massachsuetts, Amherst fanwar@umass.edu |
| Pseudocode | No | The paper describes the algorithms and their modifications but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/momin-ahmad-khan/HYDRA-FL. |
| Open Datasets | Yes | Datasets and Models: We conduct our experiments over three popular datasets: MNIST, CIFAR10, and CIFAR100. ... [20] [18] |
| Dataset Splits | Yes | CIFAR10 [18]: CIFAR10 is a 10-class classification task with 60,000 total RGB images, each of size 32x32. Each class has 6000 training images and 1000 testing images. |
| Hardware Specification | Yes | We used Py Torch [37] for our implementation on an 8GB NVIDIA RTX 3060 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch [37]' but does not specify a version number for it or any other software dependency. |
| Experiment Setup | Yes | For Fed NTD, we use 100 clients with a sampling ratio of 0.1, i.e., 10 clients are selected every round. We use momentum SGD with an initial learning rate of 0.1, weight decay of 1e5, batch size of 50, and momentum of 0.9. Each run consists of 200 rounds with 5 local epochs. |