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 4101–4150 of 6771 papers

TitleStatusHype
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective—0
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study—0
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters—0
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization—0
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization—0
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation—0
Understanding the Interplay between Privacy and Robustness in Federated Learning—0
Understanding Unintended Memorization in Federated Learning—0
Understanding Unintended Memorization in Language Models Under Federated Learning—0
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning—0
UNIDEAL: Curriculum Knowledge Distillation Federated Learning—0
Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains—0
Unified Group Fairness on Federated Learning—0
Unifying Distillation with Personalization in Federated Learning—0
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation—0
Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization—0
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration—0
Universal Medical Imaging Model for Domain Generalization with Data Privacy—0
Unlearning Clients, Features and Samples in Vertical Federated Learning—0
Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter—0
Unlocking FedNL: Self-Contained Compute-Optimized Implementation—0
Unlocking the Potential of Federated Learning for Deeper Models—0
Unlocking the Potential of Model Calibration in Federated Learning—0
Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation—0
Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies—0
Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning—0
Unsupervised anomalies detection in IIoT edge devices networks using federated learning—0
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms—0
Unsupervised Federated Learning is Possible—0
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels—0
Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks—0
Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning—0
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation—0
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation—0
Upcycling Noise for Federated Unlearning—0
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge—0
Update Compression for Deep Neural Networks on the Edge—0
Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices—0
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation—0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning—0
Use of Federated Learning and Blockchain towards Securing Financial Services—0
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks—0
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data—0
User-Centric Federated Learning—0
User-Centric Federated Learning: Trading off Wireless Resources for Personalization—0
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments—0
User Scheduling for Federated Learning Through Over-the-Air Computation—0
Using adversarial images to improve outcomes of federated learning for non-IID data—0
Using Decentralized Aggregation for Federated Learning with Differential Privacy—0
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?—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