EMNIST

EMNIST is a dataset that contains handwritten character samples and is used to evaluate the performance of machine learning models in recognizing and classifying these characters.

Papers using EMNIST (26)

Federated Domain Generalization with Data-free On-server Matching Gradient2025 · 1 citesFractional-Order Federated Learning2026FedZMG: Efficient Client-Side Optimization in Federated Learning2026Federated Learning With L0 Constraint Via Probabilistic Gates For Sparsity2025Energy and Memory-Efficient Federated Learning With Ordered Layer Freezing2025Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?2025Optimizing Split Federated Learning with Unstable Client Participation2025Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection2025Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions2024Can You Really Backdoor Federated Learning?2019 · 369 citesLotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets2020 · 73 citesParameterized Knowledge Transfer for Personalized Federated Learning2021 · 25 citesFedJAX: Federated learning simulation with JAX2021 · 20 citesSparse Random Networks for Communication-Efficient Federated Learning2022 · 8 citesShare Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning2023 · 6 citesFederated Asymptotics: a model to compare federated learning algorithms2021 · 2 citesFedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity2022 · 2 citesCommunication-Efficient Federated Learning via Optimal Client Sampling2020 · 1 citesFedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain2023 · 1 citesLearning from straggler clients in federated learning2024 · 1 citesPrecision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning2024 · 1 citesFast Server Learning Rate Tuning for Coded Federated Dropout2022Sniper Backdoor: Single Client Targeted Backdoor Attack in Federated Learning2022Learning to Generate Image Embeddings with User-level Differential Privacy2022Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing2022Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems2023