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 201250 of 6771 papers

TitleStatusHype
Proportional Fairness in Federated LearningCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data ModelingCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Non-IID Quantum Federated Learning with One-shot Communication ComplexityCode1
Bayesian Framework for Gradient LeakageCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationCode1
Data Valuation and Detections in Federated LearningCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
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
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Analyzing Federated Learning through an Adversarial LensCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI ModalitiesCode1
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence GuaranteeCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Continual Local Training for Better Initialization of Federated ModelsCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
Can Textual Gradient Work in Federated Learning?Code1
CaPC Learning: Confidential and Private Collaborative LearningCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
Enhancing Efficiency in Multidevice Federated Learning through Data SelectionCode1
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based OptimizationCode1
FedCD: Improving Performance in non-IID Federated LearningCode1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offCode1
FedCMR: Federated Cross-Modal RetrievalCode1
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
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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