CIFAR-100
Canonical168papers using it
2017first seen
Like CIFAR-10 but with 100 fine classes (grouped into 20 superclasses), 600 images each.
Papers using CIFAR-100 (168)
- QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated LearningSeparate Aggregation of Split Network for Personalized Federated LearningAFBS:Buffer Gradient Selection in Semi-asynchronous Federated 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 SelectionFederated Domain Generalization with Data-free On-server Matching GradientFlashbackCL: Mitigating Temporal Forgetting in Federated LearningAccurate and Resource-Efficient Federated Continual LearningEnabling Federated Inference via Unsupervised Consensus EmbeddingAdaptive Selection of LoRA Components in Privacy-Preserving Federated LearningEnhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability AnalysisPCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning SystemsQ-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated LearningFederated Martingale Posterior SampingTSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge NetworksData-Free Contribution Estimation in Federated Learning using Gradient von Neumann EntropyFedCova: Robust Federated Covariance Learning Against Noisy LabelsFedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated LearningSI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data SharingRepurposing 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 ReconstructionQuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model QuantisationProbabilistic Federated Learning on Uncertain and Heterogeneous Data with Model PersonalizationDiffusion-Guided Semantic Consistency for Multimodal HeterogeneityHEART-PFL: Stable Personalized Federated Learning under Heterogeneity with Hierarchical Directional Alignment and Adversarial Knowledge TransferFedRandom: Sampling Consistent and Accurate Contribution Values in Federated LearningTemperature Scaling Attack Disrupting Model Confidence in Federated LearningRIFLE: 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 LearningFractional-Order Federated LearningFedZMG: Efficient Client-Side Optimization in Federated LearningDCInject: Persistent Backdoor Attacks via Frequency Manipulation in Personal Federated LearningTaming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID DataLocal Layer-wise Differential Privacy in Federated LearningSuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-NetworksDeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet TrainingDecoupled Split Learning via Auxiliary LossGeometric Prior-Guided Federated Prompt CalibrationHetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT DevicesAn Efficient Gradient-Based Inference Attack for Federated LearningEnergy and Memory-Efficient Federated Learning With Ordered Layer FreezingMemory-adaptive Depth-wise Heterogeneous Federated LearningOvA-LP: A Simple and Efficient Framework for Federated Learning on Non-IID DataFedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OODFedTopo: Topology-Informed Representation Alignment in Federated Learning under Non-I.I.D. ConditionsDOLFIN: Balancing Stability and Plasticity in Federated Continual LearningPrompt Estimation from Prototypes for Federated Prompt Tuning of Vision TransformersFedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data HeterogeneityFedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge LearningDSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure AggregationFedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated LearningToFU: Transforming How Federated Learning Systems Forget User DataNon-Linear Trajectory Modeling for Multi-Step Gradient Inversion Attacks in Federated LearningDPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge IntelligenceOn 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-HypernetworkHASSLE: A Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated LearningFedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight AveragingTowards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification DatasetTackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable AggregationP$^2$U: Progressive Precision Update For Efficient Model DistributionOrthogonal Soft Pruning for Efficient Class UnlearningLazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous DataNosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AIFederated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsOPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated LearningAIGC-assisted Federated Learning for Edge Intelligence: Architecture
Design, Research Challenges and Future DirectionsProFed: a Benchmark for Proximity-based non-IID Federated LearningPersonalized Federated Learning via Learning Dynamic GraphsFederated Learning for Diffusion ModelsNoise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless NetworksLearn More by Using Less: Distributed Learning with Energy-Constrained Devices$\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated LearningSSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated LearningA Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated LearningUnlearning during Learning: An Efficient Federated Machine Unlearning
MethodEnsemble Distillation for Robust Model Fusion in Federated LearningGroup Knowledge Transfer: Federated Learning of Large CNNs at the EdgeCollaborative Deep Learning in Fixed Topology NetworksFederated Learning Under Intermittent Client Availability and
Time-Varying Communication ConstraintsAsynchronous Federated Continual LearningNo Fear of Heterogeneity: Classifier Calibration for Federated Learning
with Non-IID DataLocal-Global Knowledge Distillation in Heterogeneous Federated Learning
with Non-IID DataEfficient passive membership inference attack in federated learningSpeeding up Heterogeneous Federated Learning with Sequentially Trained
SuperclientsMitigating Adversarial Attacks in Federated Learning with Trusted
Execution EnvironmentsFaster Federated Learning with Decaying Number of Local SGD StepsPersonalized Federated Learning with Gaussian ProcessesFedCorr: Multi-Stage Federated Learning for Label Noise CorrectionSparse Random Networks for Communication-Efficient Federated LearningVisual Prompt Based Personalized Federated LearningSubspace based Federated UnlearningResSFL: A Resistance Transfer Framework for Defending Model Inversion
Attack in Split Federated LearningShare Your Representation Only: Guaranteed Improvement of the
Privacy-Utility Tradeoff in Federated LearningFedAC: An Adaptive Clustered Federated Learning Framework for
Heterogeneous DataAddressing Client Drift in Federated Continual Learning with Adaptive
OptimizationMinimal Model Structure Analysis for Input Reconstruction in Federated
LearningFederated Learning with Spiking Neural NetworksFLIS: Clustered Federated Learning via Inference Similarity for Non-IID
Data DistributionDual Class-Aware Contrastive Federated Semi-Supervised LearningWho Leaked the Model? Tracking IP Infringers in Accountable Federated
LearningFederated Unlearning via Class-Discriminative PruningNo Fear of Classifier Biases: Neural Collapse Inspired Federated
Learning with Synthetic and Fixed ClassifierFederated Asymptotics: a model to compare federated learning algorithmsFedHiSyn: A Hierarchical Synchronous Federated Learning Framework for
Resource and Data HeterogeneityFedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for
Non-IID Data in Federated LearningCombined Use of Federated Learning and Image Encryption for
Privacy-Preserving Image Classification with Vision TransformerAsymmetrically Decentralized Federated LearningFedAnchor: Enhancing Federated Semi-Supervised Learning with Label
Contrastive Loss for Unlabeled ClientsFedD2S: Personalized Data-Free Federated Knowledge DistillationFL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank,
Task-Specific Adapter ClusteringAdaptive Quantization Resolution and Power Control for Federated
Learning over Cell-free NetworksSparse Personalized Federated LearningAccelerating Federated Learning with a Global Biased OptimiserComfetch: Federated Learning of Large Networks on Constrained Clients
via SketchingHercules: Boosting the Performance of Privacy-preserving Federated
LearningFederated Learning for Inference at Anytime and AnywherePersonalized Privacy-Preserving Framework for Cross-Silo Federated
LearningContinual Adaptation of Vision Transformers for Federated LearningDon't Memorize; Mimic The Past: Federated Class Incremental Learning
Without Episodic MemoryBlock-Wise Encryption for Reliable Vision Transformer modelsLearning from straggler clients in federated learningRethinking the Representation in Federated Unsupervised Learning with
Non-IID DataP4: Towards private, personalized, and Peer-to-Peer learningParameterizing Federated Continual Learning for Reproducible ResearchA Resource-Adaptive Approach for Federated Learning under
Resource-Constrained EnvironmentsLearning Unlabeled Clients Divergence for Federated Semi-Supervised
Learning via Anchor Model AggregationOvercoming Catastrophic Forgetting in Federated Class-Incremental
Learning via Federated Global Twin GeneratorTwo-Bit Aggregation for Communication Efficient and Differentially
Private Federated LearningSniper Backdoor: Single Client Targeted Backdoor Attack in Federated
LearningStatMix: Data augmentation method that relies on image statistics in
federated learningPersonalized Federated Learning with Multi-branch ArchitectureMagnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification
in the Presence of Data HeterogeneityDivide-and-Conquer the NAS puzzle in Resource Constrained Federated
Learning SystemsFederated Variational Inference: Towards Improved Personalization and
GeneralizationProtoFL: Unsupervised Federated Learning via Prototypical DistillationFederated Split Learning with Only Positive Labels for
resource-constrained IoT environmentNeFL: Nested Model Scaling for Federated Learning with System
Heterogeneous ClientsFedPerfix: Towards Partial Model Personalization of Vision Transformers
in Federated LearningMaximum Knowledge Orthogonality Reconstruction with Gradients in
Federated LearningFlexTrain: A Dynamic Training Framework for Heterogeneous Devices
EnvironmentsDFML: Decentralized Federated Mutual LearningText-Enhanced Data-free Approach for Federated Class-Incremental
LearningEmInspector: Combating Backdoor Attacks in Federated Self-Supervised
Learning Through Embedding InspectionA Novel Defense Against Poisoning Attacks on Federated Learning:
LayerCAM Augmented with AutoencoderPrivacy-preserving Quantification of Non-IID Degree in Federated
LearningSpiking Neural Networks in Vertical Federated Learning: Performance
Trade-offsBuffer-based Gradient Projection for Continual Federated LearningFedHide: Federated Learning by Hiding in the NeighborsFedCert: Federated Accuracy CertificationConDa: Fast Federated Unlearning with Contribution DampeningOledFL: Unleashing the Potential of Decentralized Federated Learning via
Opposite Lookahead EnhancementDisentangling data distribution for Federated LearningQuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure
Federated LearningPrivacy Drift: Evolving Privacy Concerns in Incremental LearningProFe: Communication-Efficient Decentralized Federated Learning via
Distillation and PrototypesDelayed Random Partial Gradient Averaging for Federated LearningDecentralized Federated Learning via Mutual Knowledge TransferImproving Generalization in Federated Learning by Seeking Flat MinimaPerfectly Accurate Membership Inference by a Dishonest Central Server in
Federated LearningFlamingo: Multi-Round Single-Server Secure Aggregation with Applications
to Private Federated LearningPIP: Prototypes-Injected Prompt for Federated Class Incremental LearningBoosting the Performance of Decentralized Federated Learning via
Catalyst Acceleration