MNIST
Canonical291papers using it
2017first seen
70,000 28×28 grayscale images of handwritten digits (0–9) — the classic image-classification benchmark.
Papers using MNIST (200)
- QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated LearningTowards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without DisclosureKeTS: Kernel-based Trust Segmentation against Model Poisoning AttacksGLow -- A Novel, Flower-Based Simulated Gossip Learning StrategyPractical quantum federated learning and its experimental demonstrationFederated Domain Generalization with Data-free On-server Matching GradientOn Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel ApproachFissionVAE: Federated Non-IID Image Generation with Latent Space and
Decoder DecompositionEvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated LearningDistributed Deep Variational Approach for Privacy-preserving Data ReleasePCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning SystemsByzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum AnnealersFederated Martingale Posterior SampingStatistical Limits and Efficient Algorithms for Differentially Private Federated LearningCausal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial ContributionsCan Quantum Federated Learning Withstand Circuit-Level Backdoors?Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless ChannelsCentralized vs Decentralized Federated Learning: A trade-off performance analysisA Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated LearningEnhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential PrivacySample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID DataSI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data SharingBenchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance EvaluationClient-Conditional Federated Learning via Local Training Data StatisticsByzantine-Robust Optimization under $(L_0, L_1)$-SmoothnessQuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model QuantisationFedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in UreteroscopyOn Performance Guarantees for Federated Learning with Personalized ConstraintsIncentive-Aware Federated Averaging with Performance Guarantees under Strategic ParticipationByzantine-Robust and Differentially Private Federated Optimization under Weaker AssumptionsTowards Privacy-Preserving Federated Learning using Hybrid Homomorphic EncryptionFedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching GenerationPre-Deployment Complexity Estimation for Federated Perception SystemsTinyGuard:A lightweight Byzantine Defense for Resource-Constrained Federated Learning via Statistical Update FingerprintsFedRandom: Sampling Consistent and Accurate Contribution Values in Federated LearningRIFLE: Robust Distillation-based FL for Deep Model Deployment on Resource-Constrained IoT NetworksRoughness-Informed Federated LearningASA: Adaptive Smart Agent Federated Learning via Device-Aware Clustering for Heterogeneous IoTFractional-Order Federated LearningJSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated LearningFully Decentralized Certified UnlearningD2M: 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 LearningMURIM: Multidimensional Reputation-based Incentive Mechanism for Federated LearningFederated Learning With L0 Constraint Via Probabilistic Gates For SparsityEnhancing Federated Learning Privacy with QUBOFLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated LearningPrivacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional EncryptionFedPPA: Progressive Parameter Alignment for Personalized Federated LearningFeDABoost: Fairness Aware Federated Learning with Adaptive BoostingLocal Performance vs. Out-of-Distribution Generalization: An Empirical Analysis of Personalized Federated Learning in Heterogeneous Data EnvironmentsRobQFL: Robust Quantum Federated Learning in Adversarial EnvironmentEnhancing Gradient Variance and Differential Privacy in Quantum Federated LearningDSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure AggregationDifferentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated NoisePrivacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential PrivacyFedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AIFederated Learning: An approach with Hybrid Homomorphic EncryptionPQFed: A Privacy-Preserving Quality-Controlled Federated Learning FrameworkReliable Non-Leveled Homomorphic Encryption for Web ServicesMetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse SystemsRobust Federated Learning under Adversarial Attacks via Loss-Based Client ClusteringFedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated LearningHLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger FabricZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge ProofsA Bayesian Incentive Mechanism for Poison-Resilient Federated LearningOptiGradTrust: Byzantine-Robust Federated Learning with Multi-Feature Gradient Analysis and Reinforcement Learning-Based Trust WeightingFedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated LearningQA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image QualityLoad-Aware Training Scheduling for Model Circulation-based Decentralized Federated LearningDecoding Federated Learning: The FedNAM+ Conformal RevolutionRepuNet: A Reputation System for Mitigating Malicious Clients in DFLRobust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete DataNosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AIJoint Graph Estimation and Signal Restoration for Robust Federated LearningPQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning FrameworkA Novel Algorithm for Personalized Federated Learning: Knowledge
Distillation with Weighted Combination LossOPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated LearningFederated Learning of Low-Rank One-Shot Image Detection Models in Edge
Devices with Scalable Accuracy and Compute ComplexityWhispers of Data: Unveiling Label Distributions in Federated Learning
Through Virtual Client SimulationPRISM: Privacy-Preserving Improved Stochastic Masking for Federated
Generative ModelsByzantine-Resilient Federated Learning via Distributed OptimizationEmpirical 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 LearningFederated Learning for Diffusion ModelsFedOptimus: Optimizing Vertical Federated Learning for Scalability and
EfficiencyDecoding FL Defenses: Systemization, Pitfalls, and RemediesFedSV: Byzantine-Robust Federated Learning via Shapley ValueFBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningQFAL: Quantum Federated Adversarial LearningHybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate ShiftA Novel Pearson Correlation-Based Merging Algorithm for Robust
Distributed Machine Learning with Heterogeneous DataCommunication-Efficient and Privacy-Adaptable Mechanism for Federated LearningFL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning$\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated LearningFederated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data DistributionsTackling Selfish Clients in Federated LearningA Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed
Gradient DescentDistributed Event-Based Learning via ADMMA Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated LearningUnlearning during Learning: An Efficient Federated Machine Unlearning
MethodBlockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest BehaviorOvercoming Forgetting in Federated Learning on Non-IID DataProvably Secure Federated Learning against Malicious ClientsCollaborative Deep Learning in Fixed Topology NetworksLotteryFL: Personalized and Communication-Efficient Federated Learning
with Lottery Ticket Hypothesis on Non-IID DatasetsPyVertical: A Vertical Federated Learning Framework for Multi-headed
SplitNNXOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated
LearningStochastic-Sign SGD for Federated Learning with Theoretical GuaranteesHybridAlpha: An Efficient Approach for Privacy-Preserving Federated
LearningRobust Blockchained Federated Learning with Model Validation and
Proof-of-Stake Inspired ConsensusFederated Knowledge DistillationFed-Focal Loss for imbalanced data classification in Federated LearningLDP-FL: Practical Private Aggregation in Federated Learning with Local
Differential PrivacyFedGAN: Federated Generative Adversarial Networks for Distributed DataPractical Defences Against Model Inversion Attacks for Split Neural
NetworksFedGroup: Efficient Clustered Federated Learning via Decomposed
Data-Driven MeasureSecuring Secure Aggregation: Mitigating Multi-Round Privacy Leakage in
Federated LearningPersonalized Federated Learning via Variational Bayesian InferenceFlexible Clustered Federated Learning for Client-Level Data Distribution
ShiftMulti-Task Federated Learning for Personalised Deep Neural Networks in
Edge ComputingIncentives for Federated Learning: a Hypothesis Elicitation ApproachEnergy-Aware Analog Aggregation for Federated Learning with Redundant
DataDistributed Non-Convex Optimization with Sublinear Speedup under
Intermittent Client AvailabilityEIFFeL: Ensuring Integrity for Federated LearningAccelerating Federated Learning via Momentum Gradient DescentDecentralized Federated Learning: Balancing Communication and Computing
CostsSplitfed learning without client-side synchronization: Analyzing
client-side split network portion size to overall performance2CP: Decentralized Protocols to Transparently Evaluate Contributivity in
Blockchain Federated Learning EnvironmentsFine-Grained Data Selection for Improved Energy Efficiency of Federated
Edge LearningSecuring Federated Learning against Overwhelming Collusive AttackersDifferentially Private Federated Learning with Laplacian SmoothingProvable Defense against Privacy Leakage in Federated Learning from
Representation PerspectiveBlockchain Assisted Decentralized Federated Learning (BLADE-FL):
Performance Analysis and Resource AllocationFederated Semi-Supervised Learning with Class Distribution MismatchOn Feasibility of Server-side Backdoor Attacks on Split LearningEnhancing Federated Learning Convergence with Dynamic Data Queue and
Data Entropy-driven Participant SelectionOn Addressing Heterogeneity in Federated Learning for Autonomous
Vehicles Connected to a Drone OrchestratorDeep Reinforcement Learning Assisted Federated Learning Algorithm for
Data Management of IIoTSparse Random Networks for Communication-Efficient Federated LearningBias-Free FedGAN: A Federated Approach to Generate Bias-Free DatasetsSCOTCH: An Efficient Secure Computation Framework for Secure AggregationWarmup and Transfer Knowledge-Based Federated Learning Approach for IoT
Continuous AuthenticationSubspace based Federated UnlearningIntelligent Client Selection for Federated Learning using Cellular
AutomataMitigating Sybil Attacks on Differential Privacy based Federated
LearningMulti-VFL: A Vertical Federated Learning System for Multiple Data and
Label OwnersDifferentially Private Federated Learning via Inexact ADMM with Multiple
Local UpdatesBlockchain Assisted Decentralized Federated Learning (BLADE-FL) with
Lazy ClientsGenetic CFL: Optimization of Hyper-Parameters in Clustered Federated
LearningBoosting Federated Learning Convergence with Prototype RegularizationSelf-organizing Democratized Learning: Towards Large-scale Distributed
Learning SystemsEstimation of Individual Device Contributions for Incentivizing
Federated LearningMinimal Model Structure Analysis for Input Reconstruction in Federated
LearningCodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure
Aggregation in Federated LearningAggregation Service for Federated Learning: An Efficient, Secure, and
More Resilient RealizationSelf-Aware Personalized Federated LearningDPD-fVAE: Synthetic Data Generation Using Federated Variational
Autoencoders With Differentially-Private DecoderLOKI: Large-scale Data Reconstruction Attack against Federated Learning
through Model ManipulationPrivacy is What We Care About: Experimental Investigation of Federated
Learning on Edge DevicesFAT: Federated Adversarial TrainingA Coalition Formation Game Approach for Personalized Federated LearningEfficient Fully Distributed Federated Learning with Adaptive Local LinksFedCau: A Proactive Stop Policy for Communication and Computation
Efficient Federated LearningCombating Client Dropout in Federated Learning via Friend Model
SubstitutionFedBC: Calibrating Global and Local Models via Federated Learning Beyond
ConsensusQuantifying the Impact of Label Noise on Federated LearningCharacterization of the Global Bias Problem in Aerial Federated LearningFedGT: Identification of Malicious Clients in Federated Learning with
Secure AggregationTraining Latency Minimization for Model-Splitting Allowed Federated Edge
LearningFederated Learning with Anomaly Detection via Gradient and
Reconstruction AnalysisRepresentation of Federated Learning via Worst-Case Robust Optimization
TheoryReliability and Performance Assessment of Federated Learning on Clinical
Benchmark DataAdaptive Distillation for Decentralized Learning from Heterogeneous
ClientsUnifying Distillation with Personalization in Federated LearningDifferentially Private Federated Learning via Inexact ADMMUsing adversarial images to improve outcomes of federated learning for
non-IID dataFedHiSyn: A Hierarchical Synchronous Federated Learning Framework for
Resource and Data HeterogeneityQuantum Split Neural Network Learning using Cross-Channel PoolingAnoFel: Supporting Anonymity for Privacy-Preserving Federated LearningMitigating Communications Threats in Decentralized Federated Learning
through Moving Target DefenseFedFwd: Federated Learning without BackpropagationAsymmetrically Decentralized Federated LearningFLASH-RL: Federated Learning Addressing System and Static Heterogeneity
using Reinforcement LearningContribution Evaluation in Federated Learning: Examining Current
ApproachesFederated Learning with Multi-resolution Model BroadcastRandom Gradient Masking as a Defensive Measure to Deep Leakage in
Federated LearningPrivate Dataset Generation Using Privacy Preserving Collaborative
LearningWAFFLe: Weight Anonymized Factorization for Federated LearningGain without Pain: Offsetting DP-injected Nosies Stealthily in
Cross-device Federated LearningOptimizing the Numbers of Queries and Replies in Federated Learning with
Differential PrivacyFederated Learning with Downlink Device SelectionSparse Personalized Federated LearningThe Effect of Training Parameters and Mechanisms on Decentralized
Federated Learning based on MNIST DatasetData-Free Evaluation of User Contributions in Federated LearningWAFFLE: Weighted Averaging for Personalized Federated LearningPPA: Preference Profiling Attack Against Federated LearningHercules: Boosting the Performance of Privacy-preserving Federated
Learning