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 61516200 of 6771 papers

TitleStatusHype
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated LearningCode0
FilFL: Client Filtering for Optimized Client Participation in Federated LearningCode0
Accurate Forgetting for Heterogeneous Federated Continual LearningCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve ThemCode0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
Robust Smart Home Face Recognition under Starving Federated DataCode0
Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) FrameworkCode0
Federated Learning with Intermediate Representation RegularizationCode0
STDLens: Model Hijacking-Resilient Federated Learning for Object DetectionCode0
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated LearningCode0
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
MDA: Availability-Aware Federated Learning Client SelectionCode0
Fingerprint Attack: Client De-Anonymization in Federated LearningCode0
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed DatasetsCode0
Robustness analytics to data heterogeneity in edge computingCode0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
PARDON: Privacy-Aware and Robust Federated Domain GeneralizationCode0
DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank AdaptationCode0
Deep Models Under the GAN: Information Leakage from Collaborative Deep LearningCode0
FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder DecompositionCode0
Federated Learning With Individualized Privacy Through Client SamplingCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
F-KANs: Federated Kolmogorov-Arnold NetworksCode0
(FL)^2: Overcoming Few Labels in Federated Semi-Supervised LearningCode0
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural NetworksCode0
Medical Federated Model with Mixture of Personalized and Sharing ComponentsCode0
Deep Modeling and Optimization of Medical Image ClassificationCode0
Sparse Personalized Federated LearningCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object DetectionCode0
Memory-adaptive Depth-wise Heterogenous Federated LearningCode0
Memory-aware curriculum federated learning for breast cancer classificationCode0
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning FrameworkCode0
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the SpectrumCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Federated Learning with a Single Shared ImageCode0
FLaPS: Federated Learning and Privately ScalingCode0
Personalized Federated Learning via Heterogeneous Modular NetworksCode0
Masked Random Noise for Communication Efficient Federated LearningCode0
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language ModelsCode0
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated LearningCode0
Federated Learning with Additional Mechanisms on Clients to Reduce Communication CostsCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Exploiting Unintended Feature Leakage in Collaborative LearningCode0
Federated Learning via Plurality VoteCode0
UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification TasksCode0
Explicit Personalization and Local Training: Double Communication Acceleration in Federated LearningCode0
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph GenerationCode0
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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