SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Showing 14511500 of 6771 papers

TitleStatusHype
A V2X-based Privacy Preserving Federated Measuring and Learning SystemCode0
GAS: Generative Activation-Aided Asynchronous Split Federated LearningCode0
A Hybrid Approach to Privacy-Preserving Federated LearningCode0
Communication-Efficient Federated Learning via Predictive CodingCode0
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated LearningCode0
Federated Black-Box Adaptation for Semantic SegmentationCode0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy JudgmentCode0
Federated Active Learning for Target Domain GeneralisationCode0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
Fed-ensemble: Improving Generalization through Model Ensembling in Federated LearningCode0
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated LearningCode0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Adaptive Compression in Federated Learning via Side InformationCode0
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated LearningCode0
GLow -- A Novel, Flower-Based Simulated Gossip Learning StrategyCode0
Fair Resource Allocation in Federated LearningCode0
FedDebug: Systematic Debugging for Federated Learning ApplicationsCode0
Adaptive Federated Learning with Auto-Tuned ClientsCode0
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing SystemsCode0
FedCore: Straggler-Free Federated Learning with Distributed CoresetsCode0
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance ImprovementCode0
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated LearningCode0
FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language ModelsCode0
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust ClusteringCode0
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue ClassificationCode0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Federated Over-Air Subspace Tracking from Incomplete and Corrupted DataCode0
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guaranteesCode0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated LearningCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective OptimizationCode0
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
Continual Adaptation of Vision Transformers for Federated LearningCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
Federated Causal Discovery From InterventionsCode0
FedCert: Federated Accuracy CertificationCode0
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial MetaverseCode0
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised LearningCode0
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the SpectrumCode0
Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical VehiclesCode0
Deep Models Under the GAN: Information Leakage from Collaborative Deep LearningCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
The Cost of Training Machine Learning Models over Distributed Data SourcesCode0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated LearningCode0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified