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 301350 of 6771 papers

TitleStatusHype
Communication-Efficient Federated Learning with Accelerated Client GradientCode1
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with AdapterCode1
Bayesian Framework for Gradient LeakageCode1
Communication-Efficient Federated Learning with Binary Neural NetworksCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
CRFL: Certifiably Robust Federated Learning against Backdoor AttacksCode1
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
Decentralized Federated Learning: Balancing Communication and Computing CostsCode1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
Deep Federated Learning for Autonomous DrivingCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Differentially Private Federated Learning on Heterogeneous DataCode1
Adaptive and Parallel Split Federated Learning in Vehicular Edge ComputingCode1
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge LearningCode1
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked VehiclesCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
DYNAFED: Tackling Client Data Heterogeneity with Global DynamicsCode1
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class ImbalanceCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Edge Federated Learning Via Unit-Modulus Over-The-Air ComputationCode1
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data SubspacesCode1
Omnidirectional Transfer for Quasilinear Lifelong LearningCode1
Efficient passive membership inference attack in federated learningCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health RecordsCode1
Agnostic Federated LearningCode1
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Energy-Latency Attacks via Sponge PoisoningCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
Proportional Fairness in Federated LearningCode1
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of ThingsCode1
Evaluation Framework For Large-scale Federated LearningCode1
Non-IID Quantum Federated Learning with One-shot Communication ComplexityCode1
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated LearningCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTCode1
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion AttacksCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
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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