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

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Showing 3651–3700 of 6771 papers

TitleStatusHype
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning—0
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization—0
Service Delay Minimization for Federated Learning over Mobile Devices—0
Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach—0
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning—0
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation—0
SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework—0
SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices—0
Shared MF: A privacy-preserving recommendation system—0
Sharing Models or Coresets: A Study based on Membership Inference Attack—0
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning—0
Sharp Gaussian approximations for Decentralized Federated Learning—0
Sheaf HyperNetworks for Personalized Federated Learning—0
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks—0
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone—0
Shuffled Differentially Private Federated Learning for Time Series Data Analytics—0
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs—0
Siloed Federated Learning for Multi-Centric Histopathology Datasets—0
Simeon -- Secure Federated Machine Learning Through Iterative Filtering—0
Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models—0
Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology—0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing—0
Simultaneous Wireless Information and Power Transfer for Federated Learning—0
Single-shot General Hyper-parameter Optimization for Federated Learning—0
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis—0
Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants—0
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis—0
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability—0
Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning—0
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates—0
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks—0
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks—0
Slimmable Quantum Federated Learning—0
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
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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