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 4251–4300 of 6771 papers

TitleStatusHype
Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication—0
Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm—0
Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends—0
Wireless Federated Learning with Limited Communication and Differential Privacy—0
Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation—0
Wireless Quantized Federated Learning: A Joint Computation and Communication Design—0
Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization—0
Worldwide Federated Training of Language Models—0
WPFed: Web-based Personalized Federation for Decentralized Systems—0
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning—0
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval—0
Wyner-Ziv Gradient Compression for Federated Learning—0
XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing—0
XFedHunter: An Explainable Federated Learning Framework for Advanced Persistent Threat Detection in SDN—0
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning—0
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data—0
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data—0
Privacy Risks in Reinforcement Learning for Household Robots—0
Private Networked Federated Learning for Nonsmooth Objectives—0
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity—0
Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm—0
Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey—0
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation—0
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computations—0
Zero-Shot Federated Learning with New Classes for Audio Classification—0
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks—0
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks—0
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things—0
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV—0
zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof—0
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning—0
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs—0
Zone-based Federated Learning for Mobile Sensing Data—0
zPROBE: Zero Peek Robustness Checks for Federated Learning—0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation—0
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization—0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder—0
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning—0
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
FedMe: Federated Learning via Model Exchange—0
FedMef: Towards Memory-efficient Federated Dynamic Pruning—0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning—0
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks—0
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework—0
FedMerge: Federated Personalization via Model Merging—0
FedMes: Speeding Up Federated Learning with Multiple Edge Servers—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—0
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication—0
FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning—0
Show:102550
← PrevPage 86 of 136Next →
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