CIFAR-10
Canonical401papers using it
2017first seen
60,000 32×32 color images in 10 classes — a small, standard image-classification benchmark.
Papers using CIFAR-10 (200)
- QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated LearningSeparate Aggregation of Split Network for Personalized Federated LearningRes-MIA: A Training-Free Resolution-Based Membership Inference Attack on Federated Learning ModelsDiffusion Model-Based Data Synthesis Aided Federated Semi-Supervised
LearningTowards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without DisclosureSHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated LearningPrivacy and Accuracy Implications of Model Complexity and Integration in
Heterogeneous Federated LearningFederated Learning with Workload Reduction through Partial Training of
Client Models and Entropy-Based Data SelectionReliable Imputed-Sample Assisted Vertical Federated LearningKeTS: Kernel-based Trust Segmentation against Model Poisoning AttacksGLow -- A Novel, Flower-Based Simulated Gossip Learning StrategyFederated Domain Generalization with Data-free On-server Matching GradientBoosting Asynchronous Decentralized Learning with Model FragmentationBlockFUL: Enabling Unlearning in Blockchained Federated LearningFlashbackCL: Mitigating Temporal Forgetting in Federated LearningEvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated LearningFed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical UnlearningOpenCLAW-Nexus: A Self-Reinforcing Trust Framework for Byzantine-Resilient Decentralized Federated LearningEnabling Federated Inference via Unsupervised Consensus EmbeddingEnhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability AnalysisFedOUI: OUI-Guided Client Weighting for Federated AggregationFed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated LearningOn Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated LearningPCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning SystemsByzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum AnnealersQ-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated LearningFederated Martingale Posterior SampingStatistical Limits and Efficient Algorithms for Differentially Private Federated LearningCausal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial ContributionsTSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge NetworksCan Quantum Federated Learning Withstand Circuit-Level Backdoors?BlazeFL: Fast and Deterministic Federated Learning SimulationTask2vec Readiness: Diagnostics for Federated Learning from Pre-Training EmbeddingsA Full Compression Pipeline for Green Federated Learning in Communication-Constrained EnvironmentsData-Free Contribution Estimation in Federated Learning using Gradient von Neumann EntropyEnhanced 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 DataFedCova: Robust Federated Covariance Learning Against Noisy LabelsFedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated LearningBalancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection TechniquesSI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data SharingBenchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance EvaluationRepurposing Backdoors for Good: Ephemeral Intrinsic Proofs for Verifiable Aggregation in Cross-silo Federated LearningClient-Conditional Federated Learning via Local Training Data StatisticsCA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model ReconstructionFedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID DataFederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed ScenariosProbabilistic Federated Learning on Uncertain and Heterogeneous Data with Model PersonalizationDiffusion-Guided Semantic Consistency for Multimodal HeterogeneityFedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in UreteroscopyOn Performance Guarantees for Federated Learning with Personalized ConstraintsCollaborative Adaptive Curriculum for Progressive Knowledge DistillationIncentive-Aware Federated Averaging with Performance Guarantees under Strategic ParticipationA Theoretical Framework for Energy-Aware Gradient Pruning in Federated LearningFedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching GenerationFedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated LearningStandardized Methods and Recommendations for Green Federated LearningForget to Generalize: Iterative Adaptation for Generalization in Federated LearningRobust Federated Learning via Byzantine Filtering over Encrypted UpdatesFedRandom: Sampling Consistent and Accurate Contribution Values in Federated LearningAn Adaptive Differentially Private Federated Learning Framework with Bi-level OptimizationRIFLE: Robust Distillation-based FL for Deep Model Deployment on Resource-Constrained IoT NetworksRoughness-Informed Federated LearningMixture of Predefined Experts: Maximizing Data Usage on Vertical Federated LearningASA: Adaptive Smart Agent Federated Learning via Device-Aware Clustering for Heterogeneous IoTFractional-Order Federated LearningDCInject: Persistent Backdoor Attacks via Frequency Manipulation in Personal Federated LearningPrivate and Robust Contribution Evaluation in Federated LearningJSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated LearningTackling Privacy Heterogeneity in Differentially Private Federated LearningFedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous EnvironmentsLocal Layer-wise Differential Privacy in Federated LearningSuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-NetworksDP-FedSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information MatrixClustering-Based User Selection in Federated Learning: Metadata Exploitation for 3GPP NetworksDecoupled Split Learning via Auxiliary LossPrediction-space knowledge markets for communication-efficient federated learning on multimedia tasksStudying Various Activation Functions and Non-IID Data for Machine Learning Model RobustnessBreaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model RestorationFully Decentralized Certified UnlearningHetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT DevicesD2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative LearningSpectralKrum: A Spectral-Geometric Defense Against Byzantine Attacks in Federated LearningCost-TrustFL: Cost-Aware Hierarchical Federated Learning with Lightweight Reputation Evaluation across Multi-CloudEnergy and Memory-Efficient Federated Learning With Ordered Layer FreezingMemory-adaptive Depth-wise Heterogeneous Federated LearningEdge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OODParameter-Efficient and Personalized Federated Training of Generative Models at the EdgeFedTopo: Topology-Informed Representation Alignment in Federated Learning under Non-I.I.D. ConditionsFLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated LearningAdaptive Federated Learning Defences via Trust-Aware Deep Q-NetworksFedPPA: Progressive Parameter Alignment for Personalized Federated LearningCLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure 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 EnvironmentsFedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data HeterogeneityEnhancing Gradient Variance and Differential Privacy in Quantum Federated LearningFedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge LearningDSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure AggregationMAUI: Reconstructing Private Client Data in Federated Transfer LearningHigh-Energy Concentration for Federated Learning in Frequency DomainFedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated LearningToFU: Transforming How Federated Learning Systems Forget User DataOptimizing Split Federated Learning with Unstable Client ParticipationPQFed: A Privacy-Preserving Quality-Controlled Federated Learning FrameworkDPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge IntelligenceFedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV NetworksLightweight and Robust Federated Data ValuationReliable Non-Leveled Homomorphic Encryption for Web ServicesStabilizing Federated Learning under Extreme Heterogeneity with HeteRo-SelectBeyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated LearningDOPA: Stealthy and Generalizable Backdoor Attacks from a Single Client under Challenging Federated ConstraintsMetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse SystemsOn the Fast Adaptation of Delayed Clients in Decentralized Federated Learning: A Centroid-Aligned Distillation ApproachDecoupled Contrastive Learning for Federated LearningpFedDSH: Enabling Knowledge Transfer in Personalized Federated Learning through Data-free Sub-HypernetworkA Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated LearningRobust 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 FabricFedGA: A Fair Federated Learning Framework Based on the Gini CoefficientCaching Techniques for Reducing the Communication Cost of Federated Learning in IoT EnvironmentsFedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight AveragingOptiGradTrust: Byzantine-Robust Federated Learning with Multi-Feature Gradient Analysis and Reinforcement Learning-Based Trust WeightingAdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer SparingTowards Collaborative Fairness in Federated Learning Under Imbalanced Covariate ShiftFedABC: Attention-Based Client Selection for Federated Learning with Long-Term ViewOptimal Transport-based Domain Alignment as a Preprocessing Step for Federated LearningMobility-Aware Asynchronous Federated Learning with Dynamic SparsificationFedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated LearningLoad-Aware Training Scheduling for Model Circulation-based Decentralized Federated LearningTackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable AggregationOrthogonal Soft Pruning for Efficient Class UnlearningRepuNet: A Reputation System for Mitigating Malicious Clients in DFLNosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AIJoint Graph Estimation and Signal Restoration for Robust Federated LearningIncentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data OwnersAddressing Data Quality Decompensation in Federated Learning via Dynamic Client SelectionFederated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsOPUS-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 ComplexityHubs and Spokes Learning: Efficient and Scalable Collaborative Machine
LearningCommunication-Efficient Device Scheduling for Federated Learning Using
Lyapunov OptimizationPRISM: Privacy-Preserving Improved Stochastic Masking for Federated
Generative ModelsTowards Explainable Privacy Preservation in Federated Learning via Shapley Value-Guided Noise InjectionFedSAF: A Federated Learning Framework for Enhanced Gastric Cancer
Detection and Privacy PreservationAIGC-assisted Federated Learning for Edge Intelligence: Architecture
Design, Research Challenges and Future DirectionsProFed: 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 GraphsFederated Learning for Diffusion ModelsByzantine Resilient Federated Multi-Task Representation LearningNoise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless NetworksFedOptimus: Optimizing Vertical Federated Learning for Scalability and
EfficiencyFinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy RiskDecentralized and Robust Privacy-Preserving Model Using
Blockchain-Enabled Federated Deep Learning in Intelligent EnterprisesLearn More by Using Less: Distributed Learning with Energy-Constrained DevicesLightweight Federated Learning with Differential Privacy and Straggler
ResilienceHybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate ShiftDecentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative LearningPartial Knowledge Distillation for Alleviating the Inherent Inter-Class
Discrepancy in Federated Learning$\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated LearningTackling Selfish Clients in Federated LearningDynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated LearningSSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated LearningDistributed 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
MethodHarnessing Increased Client Participation with Cohort-Parallel Federated
LearningPassive Inference Attacks on Split Learning via Adversarial
RegularizationFederated Learning with Non-IID DataDeep Gradient Compression: Reducing the Communication Bandwidth for
Distributed TrainingMeasuring the Effects of Non-Identical Data Distribution for Federated
Visual ClassificationEnsemble Distillation for Robust Model Fusion in Federated LearningGroup Knowledge Transfer: Federated Learning of Large CNNs at the EdgeCollaborative Deep Learning in Fixed Topology NetworksLotteryFL: Personalized and Communication-Efficient Federated Learning
with Lottery Ticket Hypothesis on Non-IID DatasetsMitigating Backdoor Attacks in Federated LearningStochastic-Sign SGD for Federated Learning with Theoretical GuaranteesNo Fear of Heterogeneity: Classifier Calibration for Federated Learning
with Non-IID DataLocal-Global Knowledge Distillation in Heterogeneous Federated Learning
with Non-IID DataDENSE: Data-Free One-Shot Federated LearningLDP-FL: Practical Private Aggregation in Federated Learning with Local
Differential PrivacyFedGAN: Federated Generative Adversarial Networks for Distributed DataFedCD: Improving Performance in non-IID Federated LearningTowards General Deep Leakage in Federated LearningD2P-Fed: Differentially Private Federated Learning With Efficient
CommunicationSecuring Secure Aggregation: Mitigating Multi-Round Privacy Leakage in
Federated LearningPersonalized Federated Learning via Variational Bayesian InferenceMulti-Task Federated Learning for Personalised Deep Neural Networks in
Edge ComputingIncentives for Federated Learning: a Hypothesis Elicitation ApproachFL-WBC: Enhancing Robustness against Model Poisoning Attacks in
Federated Learning from a Client PerspectiveSpeeding up Heterogeneous Federated Learning with Sequentially Trained
SuperclientsMitigating Adversarial Attacks in Federated Learning with Trusted
Execution EnvironmentsQuasi-Global Momentum: Accelerating Decentralized Deep Learning on
Heterogeneous DataFedCVT: Semi-supervised Vertical Federated Learning with Cross-view
TrainingFederated Hyperparameter Tuning: Challenges, Baselines, and Connections
to Weight-SharingPersonalized Federated Learning with Gaussian ProcessesDecentralized Federated Learning: Balancing Communication and Computing
Costs