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 [1].
Mixed Nash for Robust Federated Learning
Authors: Wanyun Xie, Thomas Pethick, Ali Ramezani-Kebrya, Volkan Cevher
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results under challenging attacks show that Robust Tailor performs close to an upper bound with perfect knowledge of honest clients. ... Our empirical results demonstrate that Robust Tailor provides high resilience to training-time attacks while maintaining stable performance even under a challenging new mixed attack strategy. ... In this section, we evaluate the resilience of Robust Tailor against tailored attacks. ... For extensive experiments, we train the CNN model on Fashion-MNIST (FMNIST) (Xiao et al., 2017) and CIFAR10 (Krizhevsky & Hinton, 2009) datasets. |
| Researcher Affiliation | Academia | Wanyun Xie EMAIL Laboratory for Information and Inference Systems (LIONS), EPFL Thomas Pethick EMAIL Laboratory for Information and Inference Systems (LIONS), EPFL Ali Ramezani-Kebrya EMAIL Department of Informatics, University of Oslo and Visual Intelligence Centre Integreat, Norwegian Centre for Knowledge-driven Machine Learning Volkan Cevher EMAIL Laboratory for Information and Inference Systems (LIONS), EPFL |
| Pseudocode | Yes | Algorithm 1 Robust Tailor Algorithm 2 Server s aggregation Algorithm 3 Hypothetical process of aggregation Algorithm 4 Exp3 Algorithm 5 Attack Tailor Algorithm 6 Adversary s attack |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the authors have made their source code publicly available for the described methodology. |
| Open Datasets | Yes | We train a CNN model on MNIST (Lecun et al., 1998) under independent and identically distributed (iid) setting. ... For extensive experiments, we train the CNN model on Fashion-MNIST (FMNIST) (Xiao et al., 2017) and CIFAR10 (Krizhevsky & Hinton, 2009) datasets. |
| Dataset Splits | Yes | Both MNIST (Lecun et al., 1998) and FMNIST (Xiao et al., 2017) datasets contain 60000 training samples and 10000 test samples. |
| Hardware Specification | Yes | All experiments have been run on a cluster with Xeon-Gold processors and V100 GPUs. |
| Software Dependencies | No | The paper mentions training a CNN model and uses standard datasets, but does not provide specific software versions for libraries like TensorFlow, PyTorch, scikit-learn, etc. |
| Experiment Setup | Yes | Table 1: Training hyper-parameters for MNIST, FMNIST, and CIFAR10 Hyper-parameter MNIST FMNIST CIFAR10 Learning Rate 0.01 0.003 0.002 Batch Size 50 50 80 Total Iterations 15K 10K 10K K 10 10 10 λ1, λ2 0.3 0.3 0.3 λ1, λ2 0.3 0.3 0.3 |