F-MNIST

F-MNIST (Fashion-MNIST) is a dataset that contains grayscale images of clothing items and is used to evaluate the performance of machine learning models, particularly in the context of backdoor attacks in federated learning.

Papers using F-MNIST (23)

CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction2026FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation2026FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning2026Clustering-Based User Selection in Federated Learning: Metadata Exploitation for 3GPP Networks2026MURIM: Multidimensional Reputation-based Incentive Mechanism for Federated Learning2025FedPPA: Progressive Parameter Alignment for Personalized Federated Learning2025FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI2025Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering2025FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning2025PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models2025FedOptimus: Optimizing Vertical Federated Learning for Scalability and Efficiency2025Personalized Federated Learning via Variational Bayesian Inference2022 · 19 citesOn Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator2021 · 8 citesFedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning2021 · 6 citesTCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels2022 · 5 citesFLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution2022 · 4 citesSparse Personalized Federated Learning2021 · 1 citesFedward: Flexible Federated Backdoor Defense Framework with Non-IID Data2023 · 1 citesEquitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment2023 · 1 citesPersonalized Federated Learning with Attention-based Client Selection2023 · 1 citesSniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning2022FedCliP: Federated Learning with Client Pruning2023FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning2023