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 13511400 of 6771 papers

TitleStatusHype
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting0
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving0
Quantum delegated and federated learning via quantum homomorphic encryption0
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Privacy Attack in Federated Learning is Not Easy: An Experimental Study0
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning0
An Enhanced Federated Prototype Learning Method under Domain Shift0
In-depth Analysis of Privacy Threats in Federated Learning for Medical DataCode0
Hierarchical Federated ADMM0
Hierarchical Federated Learning with Multi-Timescale Gradient CorrectionCode0
HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting0
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration0
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs0
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge IntegrationCode0
PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models0
Federated Learning under Attack: Improving Gradient Inversion for Batch of Images0
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD0
Byzantine-Robust Aggregation for Securing Decentralized Federated LearningCode0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data0
Efficient Federated Learning against Heterogeneous and Non-stationary Client UnavailabilityCode0
Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks0
Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning0
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated LearningCode0
Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique0
Federated Large Language Models: Current Progress and Future Directions0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications0
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review0
FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning0
A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning0
Personalized Federated Learning via Backbone Self-Distillation0
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT0
Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems0
Federated Graph Learning with Adaptive Importance-based Sampling0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Energy-Aware Federated Learning in Satellite Constellations0
Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping0
SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated LearningCode0
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization0
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks0
FedSlate:A Federated Deep Reinforcement Learning Recommender SystemCode0
Floating-floating point: a highly accurate number representation with flexible Counting ranges0
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning0
Recovering Global Data Distribution Locally in Federated Learning0
FeDETR: a Federated Approach for Stenosis Detection in Coronary Angiography0
Noise-Robust and Resource-Efficient ADMM-based Federated Learning0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness0
Federated Learning with Label-Masking DistillationCode1
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization0
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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