FEMNIST

Dataset Card for FEMNIST The FEMNIST dataset is a part of the LEAF benchmark. It represents image classification of handwritten digits, lower and uppercase letters, giving 62 unique labels. Dataset Details Dataset Description Each sample is comprised of a (28x28) grayscale image, writer_id, hsf_id, and character. Curat

Papers using FEMNIST (59)

Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning2023 · 2 citesExploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning2026Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings2026Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy2026FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learning2026Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation2026Robust Federated Learning via Byzantine Filtering over Encrypted Updates2026Fractional-Order Federated Learning2026Prediction-space knowledge markets for communication-efficient federated learning on multimedia tasks2025Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration2025Cost-TrustFL: Cost-Aware Hierarchical Federated Learning with Lightweight Reputation Evaluation across Multi-Cloud2025CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning2025FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting2025Non-Linear Trajectory Modeling for Multi-Step Gradient Inversion Attacks in Federated Learning2025Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning2025FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning2025Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners2025Towards Explainable Privacy Preservation in Federated Learning via Shapley Value-Guided Noise Injection2025Aggregation on Learnable Manifolds for Asynchronous Federated Optimization2025Byzantine Resilient Federated Multi-Task Representation Learning2025Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks2025Decentralized and Robust Privacy-Preserving Model Using Blockchain-Enabled Federated Deep Learning in Intelligent Enterprises2025Harnessing Increased Client Participation with Cohort-Parallel Federated Learning2024TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients2024 · 6 citesAbnormal Client Behavior Detection in Federated Learning2019 · 112 citesCSAFL: A Clustered Semi-Asynchronous Federated Learning Framework2021 · 47 citesFed-Focal Loss for imbalanced data classification in Federated Learning2020 · 34 citesFedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure2020 · 20 citesFlexible Clustered Federated Learning for Client-Level Data Distribution Shift2021 · 18 citesEnforcing fairness in private federated learning via the modified method of differential multipliers2021 · 18 citesData Leakage in Federated Averaging2022 · 15 citesFederated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing2021 · 12 citesFaster Federated Learning with Decaying Number of Local SGD Steps2023 · 12 citesHeterogeneous Data-Aware Federated Learning2020 · 8 citesWarmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication2022 · 7 citesDifferentially Private Federated Learning via Inexact ADMM with Multiple Local Updates2022 · 6 citesSubject Granular Differential Privacy in Federated Learning2022 · 6 citesFedVal: Different good or different bad in federated learning2023 · 6 citesSelf-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems2020 · 4 citesSelf-Aware Personalized Federated Learning2022 · 4 citesDifferentially Private Federated Learning via Inexact ADMM2021 · 2 citesFedD2S: Personalized Data-Free Federated Knowledge Distillation2024 · 2 citesToward Understanding the Influence of Individual Clients in Federated Learning2020 · 1 citesOptimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy2021 · 1 citesAccelerating Federated Learning with a Global Biased Optimiser2021 · 1 citesA Fast Blockchain-based Federated Learning Framework with Compressed Communications2022 · 1 citesFederated Learning for Inference at Anytime and Anywhere2022 · 1 citesRevisiting Personalized Federated Learning: Robustness Against Backdoor Attacks2023 · 1 citesFedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality2024 · 1 citesPersonalized federated learning based on feature fusion2024 · 1 citesCommunication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization2022Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training2022FedVQCS: Federated Learning via Vector Quantized Compressed Sensing2022Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data2022An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication2022Federated Variational Inference: Towards Improved Personalization and Generalization2023Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information2024BaFFLe: Backdoor detection via Feedback-based Federated Learning2020Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training2024