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 551600 of 6771 papers

TitleStatusHype
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
CRFL: Certifiably Robust Federated Learning against Backdoor AttacksCode1
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative LearningCode1
DAGER: Exact Gradient Inversion for Large Language ModelsCode1
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge DevicesCode1
TabLeak: Tabular Data Leakage in Federated LearningCode1
Data Poisoning Attacks Against Federated Learning SystemsCode1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
DeceFL: A Principled Decentralized Federated Learning FrameworkCode1
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
Bayesian Framework for Gradient LeakageCode1
Differentially private cross-silo federated learningCode1
Differentially Private Federated Learning on Heterogeneous DataCode1
Differentially Private Learning with Adaptive ClippingCode1
Distantly Supervised Relation Extraction in Federated SettingsCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient DescentCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
A Federated Data-Driven Evolutionary AlgorithmCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Dual-Personalizing Adapter for Federated Foundation ModelsCode1
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRICode1
FedGiA: An Efficient Hybrid Algorithm for Federated LearningCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Efficient passive membership inference attack in federated learningCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
End-to-End Evaluation of Federated Learning and Split Learning for Internet of ThingsCode1
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-BoostingCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Evaluating Gradient Inversion Attacks and Defenses in Federated LearningCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
Evaluation Framework For Large-scale Federated LearningCode1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation ModelsCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated DistillationCode1
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion AttacksCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Fast Federated Learning by Balancing Communication Trade-OffsCode1
Fast Federated Learning in the Presence of Arbitrary Device UnavailabilityCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
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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