Fashion-MNIST
Canonical93papers using it
2020first seen
A drop-in MNIST replacement with 70,000 grayscale images across 10 clothing categories.
Papers using Fashion-MNIST (93)
- QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated LearningSeparate Aggregation of Split Network for Personalized Federated LearningKeTS: Kernel-based Trust Segmentation against Model Poisoning AttacksFissionVAE: Federated Non-IID Image Generation with Latent Space and
Decoder DecompositionBlockFUL: Enabling Unlearning in Blockchained Federated LearningFed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated LearningPCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning SystemsCausal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial ContributionsBalancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection TechniquesClient-Conditional Federated Learning via Local Training Data StatisticsTinyGuard:A lightweight Byzantine Defense for Resource-Constrained Federated Learning via Statistical Update FingerprintsFedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous EnvironmentsQFed: Parameter-Compact Quantum-Classical Federated LearningFederated Learning Under Temporal Drift -- Mitigating Catastrophic Forgetting via Experience ReplayD2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative LearningEvaluation Framework for Centralized and Decentralized Aggregation Algorithm in Federated SystemsALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated LearningEdge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?FedTopo: Topology-Informed Representation Alignment in Federated Learning under Non-I.I.D. ConditionsRobQFL: Robust Quantum Federated Learning in Adversarial EnvironmentDifferentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated NoiseA Coopetitive-Compatible Data Generation Framework for Cross-silo Federated LearningLightweight and Robust Federated Data ValuationBeyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated LearningA Bayesian Incentive Mechanism for Poison-Resilient Federated LearningAdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer SparingTowards Collaborative Fairness in Federated Learning Under Imbalanced Covariate ShiftRobust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete DataAddressing Data Quality Decompensation in Federated Learning via Dynamic Client SelectionWhispers of Data: Unveiling Label Distributions in Federated Learning
Through Virtual Client SimulationByzantine-Resilient Federated Learning via Distributed OptimizationFedSAF: A Federated Learning Framework for Enhanced Gastric Cancer
Detection and Privacy PreservationEmpirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AIProFed: a Benchmark for Proximity-based non-IID Federated LearningMulti-Objective Optimization for Privacy-Utility Balance in
Differentially Private Federated LearningPersonalized Federated Learning via Learning Dynamic GraphsFBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningDecentralized and Robust Privacy-Preserving Model Using
Blockchain-Enabled Federated Deep Learning in Intelligent EnterprisesFL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRningPartial Knowledge Distillation for Alleviating the Inherent Inter-Class
Discrepancy in Federated LearningFederated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data DistributionsA Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated LearningMitigating Backdoor Attacks in Federated LearningLDP-FL: Practical Private Aggregation in Federated Learning with Local
Differential PrivacyFlexible Clustered Federated Learning for Client-Level Data Distribution
ShiftFL-WBC: Enhancing Robustness against Model Poisoning Attacks in
Federated Learning from a Client PerspectiveComputational Intelligence and Deep Learning for Next-Generation
Edge-Enabled Industrial IoTSecuring Federated Learning against Overwhelming Collusive AttackersBlockchain Assisted Decentralized Federated Learning (BLADE-FL):
Performance Analysis and Resource AllocationDeep Reinforcement Learning Assisted Federated Learning Algorithm for
Data Management of IIoTBias-Free FedGAN: A Federated Approach to Generate Bias-Free DatasetsMulti-VFL: A Vertical Federated Learning System for Multiple Data and
Label OwnersPersonalized Over-the-Air Federated Learning with Personalized
Reconfigurable Intelligent SurfacesBlockchain Assisted Decentralized Federated Learning (BLADE-FL) with
Lazy ClientsFedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning
and Negative LearningBoosting Federated Learning Convergence with Prototype RegularizationSelf-organizing Democratized Learning: Towards Large-scale Distributed
Learning SystemsDPD-fVAE: Synthetic Data Generation Using Federated Variational
Autoencoders With Differentially-Private DecoderFAT: Federated Adversarial TrainingA Coalition Formation Game Approach for Personalized Federated LearningPersonalized Quantum Federated Learning for Privacy Image ClassificationAdaptive Quantization Resolution and Power Control for Federated
Learning over Cell-free NetworksWAFFLe: Weight Anonymized Factorization for Federated LearningPFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsFabricated Flips: Poisoning Federated Learning without DataPersonalized Privacy-Preserving Framework for Cross-Silo Federated
LearningFederated Learning Model Aggregation in Heterogenous Aerial and Space
NetworksBinary Federated Learning with Client-Level Differential PrivacyFLEDGE: Ledger-based Federated Learning Resilient to Inference and
Backdoor AttacksPrivacy-Preserving Aggregation for Decentralized Learning with
Byzantine-RobustnessData Similarity-Based One-Shot Clustering for Multi-Task Hierarchical
Federated LearningDynamic Defense Against Byzantine Poisoning Attacks in Federated
LearningCatFedAvg: Optimising Communication-efficiency and Classification
Accuracy in Federated LearningPrior-Independent Auctions for the Demand Side of Federated LearningInformation Stealing in Federated Learning Systems Based on Generative
Adversarial NetworksCoding for Straggler Mitigation in Federated LearningTwo-Bit Aggregation for Communication Efficient and Differentially
Private Federated LearningDACFL: Dynamic Average Consensus Based Federated Learning in
Decentralized TopologyMagnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification
in the Presence of Data HeterogeneityFedDefender: Backdoor Attack Defense in Federated LearningPerformance Analysis for Resource Constrained Decentralized Federated
Learning Over Wireless NetworksALI-DPFL: Differentially Private Federated Learning with Adaptive Local
IterationsFedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning
CommunicationsMitigating System Bias in Resource Constrained Asynchronous Federated
Learning SystemsPrivacy-Preserving, Dropout-Resilient Aggregation in Decentralized
LearningRobust Model Aggregation for Heterogeneous Federated Learning: Analysis
and OptimizationsDecaf: Data Distribution Decompose Attack against Federated LearningFedDM: Enhancing Communication Efficiency and Handling Data
Heterogeneity in Federated Diffusion ModelsTraining on Fake Labels: Mitigating Label Leakage in Split Learning via
Secure Dimension TransformationClient-Side Patching against Backdoor Attacks in Federated LearningOn the Robustness of Distributed Machine Learning against Transfer
AttacksDecentralized Federated Learning via Mutual Knowledge TransferPrecision-Weighted Federated Learning