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 251300 of 6771 papers

TitleStatusHype
Combating Exacerbated Heterogeneity for Robust Models in Federated LearningCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial IntelligenceCode1
Federated Learning Enables Big Data for Rare Cancer Boundary DetectionCode1
Communication-Efficient Adaptive Federated LearningCode1
Communication-Efficient Federated Learning with Accelerated Client GradientCode1
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Catastrophic Data Leakage in Vertical Federated LearningCode1
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing ModalitiesCode1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated LearningCode1
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with AdapterCode1
CRFL: Certifiably Robust Federated Learning against Backdoor AttacksCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
ByzFL: Research Framework for Robust Federated LearningCode1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Bayesian Framework for Gradient LeakageCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Bias Propagation in Federated LearningCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
A Federated Data-Driven Evolutionary AlgorithmCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRICode1
CaPC Learning: Confidential and Private Collaborative LearningCode1
A federated graph neural network framework for privacy-preserving personalizationCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
Enhancing Efficiency in Multidevice Federated Learning through Data SelectionCode1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
Collaborative Fairness in Federated LearningCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
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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