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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4401–4450 of 6771 papers

TitleStatusHype
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models—0
SLVR: Securely Leveraging Client Validation for Robust Federated Learning—0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning—0
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning—0
Smart Multi-tenant Federated Learning—0
Smart Policy Control for Securing Federated Learning Management System—0
Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning—0
Smoothed Normalization for Efficient Distributed Private Optimization—0
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning—0
Smoothly Giving up: Robustness for Simple Models—0
Smuche: Scalar-Multiplicative Caching in Homomorphic Encryption—0
Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G—0
SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks—0
Social-Aware Clustered Federated Learning with Customized Privacy Preservation—0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks—0
Sociodynamics-inspired Adaptive Coalition and Client Selection in Federated Learning—0
A Survey on Federated Unlearning: Challenges and Opportunities—0
SoK: On Gradient Leakage in Federated Learning—0
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation—0
SoK: Verifiable Cross-Silo FL—0
Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks—0
Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees—0
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning—0
Sparse Federated Learning with Hierarchical Personalized Models—0
Sparse Federated Training of Object Detection in the Internet of Vehicles—0
Sparse Incremental Aggregation in Multi-Hop Federated Learning—0
Sparse Incremental Aggregation in Satellite Federated Learning—0
Communication-Efficient Federated Learning via Regularized Sparse Random Networks—0
Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0—0
Sparse Training for Federated Learning with Regularized Error Correction—0
Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning—0
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization—0
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning—0
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks—0
SPEAR:Exact Gradient Inversion of Batches in Federated Learning—0
Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference—0
Speech Privacy Leakage from Shared Gradients in Distributed Learning—0
SPEFL: Efficient Security and Privacy Enhanced Federated Learning Against Poisoning Attacks—0
SphereFed: Hyperspherical Federated Learning—0
SPIDER: Searching Personalized Neural Architecture for Federated Learning—0
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices—0
Spiking Neural Networks in Vertical Federated Learning: Performance Trade-offs—0
Spiking Neural Networks -- Part III: Neuromorphic Communications—0
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks—0
SPIRE: Conditional Personalization for Federated Diffusion Generative Models—0
SplitAMC: Split Learning for Robust Automatic Modulation Classification—0
Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase—0
Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization—0
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance—0
SplitFed resilience to packet loss: Where to split, that is the question—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified