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 67016750 of 6771 papers

TitleStatusHype
A Differentially Private Blockchain-Based Approach for Vertical Federated LearningCode0
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model AggregationCode0
TEE-based decentralized recommender systems: The raw data sharing redemptionCode0
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated LearningCode0
Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. DataCode0
Interpretability of Fine-grained Classification of Sadness and DepressionCode0
Dropout is NOT All You Need to Prevent Gradient LeakageCode0
VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing valuesCode0
FedCore: Straggler-Free Federated Learning with Distributed CoresetsCode0
Soft-Label Caching and Sharpening for Communication-Efficient Federated DistillationCode0
Communication-Efficient Federated Learning via Predictive CodingCode0
A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical ImagingCode0
Privacy-preserving patient clustering for personalized federated learningCode0
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust ClusteringCode0
On the Outsized Importance of Learning Rates in Local Update MethodsCode0
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
Byzantine-Robust Clustered Federated LearningCode0
On the (In)security of Peer-to-Peer Decentralized Machine LearningCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated LearningCode0
On the Robustness of Distributed Machine Learning against Transfer AttacksCode0
Byzantine-Robust Aggregation for Securing Decentralized Federated LearningCode0
Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical DatasetsCode0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
IODeep: an IOD for the introduction of deep learning in the DICOM standardCode0
FedCert: Federated Accuracy CertificationCode0
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data SilosCode0
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit FeedbackCode0
Robust Heterogeneous Federated Learning under Data CorruptionCode0
IPLS : A Framework for Decentralized Federated LearningCode0
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVCode0
IronForge: An Open, Secure, Fair, Decentralized Federated LearningCode0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
On Vessel Location Forecasting and the Effect of Federated LearningCode0
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance SamplingCode0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
DRIVE: One-bit Distributed Mean EstimationCode0
DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated IndustriesCode0
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private DecoderCode0
Domain Borders Are There to Be Crossed With Federated Few-Shot AdaptationCode0
Adaptive Compression in Federated Learning via Side InformationCode0
Federated Learning over Connected ModesCode0
FedBoosting: Federated Learning with Gradient Protected Boosting for Text RecognitionCode0
Open-Vocabulary Federated Learning with Multimodal PrototypingCode0
FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language ModelsCode0
DNN gradient lossless compression: Can GenNorm be the answer?Code0
Divergence-aware Federated Self-Supervised LearningCode0
Solving a Class of Non-Convex Minimax Optimization in Federated LearningCode0
Distributionally Robust Learning for Multi-source Unsupervised Domain AdaptationCode0
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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