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 17011750 of 6771 papers

TitleStatusHype
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep LearningCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data ManipulationCode0
Randomized Quantization is All You Need for Differential Privacy in Federated LearningCode0
Clustered Federated Learning via Embedding DistributionsCode0
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy ConstraintsCode0
Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost BudgetCode0
Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulatorCode0
Clustered Federated Learning based on Nonconvex Pairwise FusionCode0
Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated LearningCode0
Ensemble Distillation for Robust Model Fusion in Federated LearningCode0
Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data DistributionsCode0
Energy Prediction using Federated LearningCode0
Enhanced Security and Privacy via Fragmented Federated LearningCode0
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous DataCode0
Evaluating Federated Kolmogorov-Arnold Networks on Non-IID DataCode0
Explicit Personalization and Local Training: Double Communication Acceleration in Federated LearningCode0
Enable the Right to be Forgotten with Federated Client Unlearning in Medical ImagingCode0
Encrypted machine learning of molecular quantum propertiesCode0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
Empowering Data Mesh with Federated LearningCode0
A Potential Game Perspective in Federated LearningCode0
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
Revealing and Protecting Labels in Distributed TrainingCode0
Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEsCode0
Communication-Efficient Federated Learning via Clipped Uniform QuantizationCode0
SemiSFL: Split Federated Learning on Unlabeled and Non-IID DataCode0
Robust Aggregation for Federated LearningCode0
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality SelectionCode0
Client-specific Property Inference against Secure Aggregation in Federated LearningCode0
DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated IndustriesCode0
Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense FrameworkCode0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Efficient Federated Learning against Heterogeneous and Non-stationary Client UnavailabilityCode0
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Robust One Round Federated Learning with Predictive Space Bayesian InferenceCode0
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares SolutionCode0
End-to-End Verifiable Decentralized Federated LearningCode0
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight GenerationCode0
Safe-EF: Error Feedback for Nonsmooth Constrained OptimizationCode0
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI ImagesCode0
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated LearningCode0
Client-Edge-Cloud Hierarchical Federated LearningCode0
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated LearningCode0
E-3SFC: Communication-Efficient Federated Learning with Double-way Features SynthesizingCode0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated LearningCode0
Towards Energy-Aware Federated Learning on Battery-Powered ClientsCode0
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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