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 4051–4100 of 6771 papers

TitleStatusHype
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting—0
Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT—0
Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection—0
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain—0
Traversal Learning Coordination For Lossless And Efficient Distributed Learning—0
Tree-based Models for Vertical Federated Learning: A Survey—0
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency—0
Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems—0
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning—0
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning—0
TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance—0
Trustformer: A Trusted Federated Transformer—0
Trustworthy Federated Learning: A Survey—0
Trustworthy Federated Learning: Privacy, Security, and Beyond—0
Trustworthy Federated Learning via Blockchain—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchain—0
Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling—0
Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems—0
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features—0
Tunable Soft Prompts are Messengers in Federated Learning—0
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning—0
Turning Privacy-preserving Mechanisms against Federated Learning—0
Turn Signal Prediction: A Federated Learning Case Study—0
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning—0
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning—0
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks—0
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction—0
UA-PDFL: A Personalized Approach for Decentralized Federated Learning—0
UAV-Aided Multi-Community Federated Learning—0
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning—0
UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing—0
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach—0
UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis—0
UAV Communications for Sustainable Federated Learning—0
UAV-Enabled Asynchronous Federated Learning—0
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data—0
UIFV: Data Reconstruction Attack in Vertical Federated Learning—0
Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation—0
Uncertainty-Aware Explainable Federated Learning—0
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning—0
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor—0
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning—0
Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review—0
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks—0
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders—0
Towards Understanding Adversarial Transferability in Federated Learning—0
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning—0
Understanding Byzantine Robustness in Federated Learning with A Black-box Server—0
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy—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