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 4201–4250 of 6771 papers

TitleStatusHype
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data—0
WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning—0
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models—0
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication—0
WassFFed: Wasserstein Fair Federated Learning—0
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring—0
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning—0
WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy—0
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks—0
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems—0
Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data—0
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging—0
Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective—0
WEST: Word Encoded Sequence Transducers—0
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling—0
What Do We Mean by Generalization in Federated Learning?—0
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System—0
When Decentralized Optimization Meets Federated Learning—0
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network—0
When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA—0
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities—0
When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection—0
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices—0
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions—0
When MiniBatch SGD Meets SplitFed Learning:Convergence Analysis and Performance Evaluation—0
When NOMA Meets AIGC: Enhanced Wireless Federated Learning—0
When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain—0
When the Curious Abandon Honesty: Federated Learning Is Not Private—0
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning—0
Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation—0
Where is the Testbed for my Federated Learning Research?—0
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning—0
Whispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation—0
White-box Inference Attacks against Centralized Machine Learning and Federated Learning—0
Why do we regularise in every iteration for imaging inverse problems?—0
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models—0
WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning—0
Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing—0
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning—0
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach—0
Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions—0
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning—0
Wireless Communications for Collaborative Federated Learning—0
Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)—0
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data—0
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design—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