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 41014150 of 6771 papers

TitleStatusHype
Federated Causal Discovery From InterventionsCode0
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks0
Over-The-Air Clustered Wireless Federated Learning0
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial MetaverseCode0
Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning0
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain0
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme0
Federated Multilingual Models for Medical Transcript Analysis0
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control0
Fairness in Federated Learning via Core-Stability0
Federated Hypergradient DescentCode0
Client Selection in Federated Learning: Principles, Challenges, and Opportunities0
FedGen: Generalizable Federated Learning for Sequential Data0
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization0
FedTP: Federated Learning by Transformer PersonalizationCode1
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single LayersCode0
A Convergence Theory for Federated Average: Beyond Smoothness0
Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems0
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach0
TorchFL: A Performant Library for Bootstrapping Federated Learning ExperimentsCode1
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks0
Multi-Resource Allocation for On-Device Distributed Federated Learning Systems0
ModularFed: Leveraging Modularity in Federated Learning FrameworksCode0
A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection0
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices0
FL Games: A Federated Learning Framework for Distribution Shifts0
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy0
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms0
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient0
VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing valuesCode0
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones0
One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular Optimization0
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction0
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance0
Federated clustering with GAN-based data synthesisCode0
Fast-Convergent Federated Learning via Cyclic AggregationCode1
Auxo: Efficient Federated Learning via Scalable Client Clustering0
GowFed -- A novel Federated Network Intrusion Detection System0
Machine Unlearning of Federated ClustersCode1
Completely Heterogeneous Federated Learning0
Federated Learning based Energy Demand Prediction with Clustered Aggregation0
Federated Learning with Intermediate Representation RegularizationCode0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism0
Differentially Private CutMix for Split Learning with Vision Transformer0
FedVMR: A New Federated Learning method for Video Moment Retrieval0
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout0
Local Model Reconstruction Attacks in Federated Learning and their Uses0
M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction0
Prototype-Based Layered Federated Cross-Modal Hashing0
Exploiting Features and Logits in Heterogeneous Federated Learning0
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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