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
FedLess: Secure and Scalable Federated Learning Using Serverless ComputingCode1
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
FedSim: Similarity guided model aggregation for Federated LearningCode1
FedFly: Towards Migration in Edge-based Distributed Federated LearningCode1
Implicit Model Specialization through DAG-based Decentralized Federated LearningCode1
Resource-Efficient Federated LearningCode1
Efficient passive membership inference attack in federated learningCode1
Improving Fairness via Federated LearningCode1
Gradient Inversion with Generative Image PriorCode1
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial OutcomesCode1
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client PerspectiveCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified ModelsCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
Towards Federated Bayesian Network Structure Learning with Continuous OptimizationCode1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud ComputingCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
Deep Federated Learning for Autonomous DrivingCode1
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive TrainingCode1
Federated Learning from Small DatasetsCode1
Communication-Efficient Federated Learning with Binary Neural NetworksCode1
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum OptimizationCode1
Personalized Retrogress-Resilient Framework for Real-World Medical Federated LearningCode1
FedIPR: Ownership Verification for Federated Deep Neural Network ModelsCode1
FedProc: Prototypical Contrastive Federated Learning on Non-IID dataCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Connecting Low-Loss Subspace for Personalized Federated LearningCode1
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning FrameworkCode1
Source Inference Attacks in Federated LearningCode1
Byzantine-robust Federated Learning through Collaborative Malicious Gradient FilteringCode1
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News RecommendationCode1
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated LearningCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Flexible Clustered Federated Learning for Client-Level Data Distribution ShiftCode1
Multi-Center Federated Learning: Clients Clustering for Better PersonalizationCode1
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated LearningCode1
Addressing Algorithmic Disparity and Performance Inconsistency in Federated LearningCode1
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular DataCode1
Collaboration Equilibrium in Federated LearningCode1
Federated Adversarial Debiasing for Fair and Transferable RepresentationsCode1
FedMatch: Federated Learning Over Heterogeneous Question Answering DataCode1
FedJAX: Federated learning simulation with JAXCode1
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence GuaranteeCode1
A General Theory for Client Sampling in Federated LearningCode1
Decentralized Federated Learning: Balancing Communication and Computing CostsCode1
FedLab: A Flexible Federated Learning FrameworkCode1
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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