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 5201–5250 of 6771 papers

TitleStatusHype
Multimodal Federated Learning on IoT Data—0
Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms—0
Multimodal Federated Learning in Healthcare: a Review—0
Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR—0
Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration—0
Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with Vision Large Language Model—0
Multimodal Online Federated Learning with Modality Missing in Internet of Things—0
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
Multi-Model Federated Learning—0
Multi-Model Federated Learning with Provable Guarantees—0
Multi-model learning by sequential reading of untrimmed videos for action recognition—0
Multi-objective Evolutionary Federated Learning—0
Multi-objective methods in Federated Learning: A survey and taxonomy—0
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication—0
Multiple Access in the Era of Distributed Computing and Edge Intelligence—0
Multiple Kernel-Based Online Federated Learning—0
Multi-Resource Allocation for On-Device Distributed Federated Learning Systems—0
Scalable Hierarchical Over-the-Air Federated Learning—0
Multi-Session Budget Optimization for Forward Auction-based Federated Learning—0
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare—0
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation—0
Multi-Target Federated Backdoor Attack Based on Feature Aggregation—0
Multi-Task and Transfer Learning for Federated Learning Applications—0
Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation—0
Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks—0
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation—0
Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks—0
Over-the-Air Federated Multi-Task Learning—0
Multi-Task Over-the-Air Federated Learning in Cell-Free Massive MIMO Systems—0
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems—0
Multi-Tier Client Selection for Mobile Federated Learning Networks—0
Multi-Tier Federated Learning for Vertically Partitioned Data—0
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners—0
Mutual Information Regularization for Vertical Federated Learning—0
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones—0
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent—0
Navigating Distribution Shifts in Medical Image Analysis: A Survey—0
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models—0
Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networks—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning—0
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection—0
Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation—0
NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing—0
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients—0
Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness—0
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data—0
Network Adaptive Federated Learning: Congestion and Lossy Compression—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