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 51515200 of 6771 papers

TitleStatusHype
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks0
Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network0
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning0
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles0
Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM0
Modality Alignment Meets Federated Broadcasting0
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation0
Mode Connectivity and Data Heterogeneity of Federated Learning0
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer0
Model-Driven Quantum Federated Learning (QFL)0
Model Extraction Attacks on Split Federated Learning0
Model Parallelism With Subnetwork Data Parallelism0
Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks0
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing0
Models of fairness in federated learning0
How to Backdoor HyperNetwork in Personalized Federated Learning?0
Modular Federated Learning0
Modular Federated Learning: A Meta-Framework Perspective0
Momentum Approximation in Asynchronous Private Federated Learning0
Momentum Benefits Non-IID Federated Learning Simply and Provably0
Momentum Gradient Descent Federated Learning with Local Differential Privacy0
More Communication Does Not Result in Smaller Generalization Error in Federated Learning0
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style0
More Industry-friendly: Federated Learning with High Efficient Design0
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks0
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models0
Movable Antenna-Aided Federated Learning with Over-the-Air Aggregation: Joint Optimization of Positioning, Beamforming, and User Selection0
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients0
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence0
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption0
Single-round Self-supervised Distributed Learning using Vision Transformer0
MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained Machines to Collaboratively Train Larger Models0
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning0
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning0
Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation0
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning0
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning0
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service0
Multi-dimensional Fair Federated Learning0
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point0
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation0
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning0
Multi-Layer Hierarchical Federated Learning with Quantization0
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data0
Multi-Message Shuffled Privacy in Federated Learning0
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports0
Show:102550
← PrevPage 104 of 136Next →

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