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 4601–4650 of 6771 papers

TitleStatusHype
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective—0
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning—0
The Poisson binomial mechanism for secure and private federated learning—0
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy—0
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers—0
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning—0
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining—0
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness—0
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning—0
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks—0
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector—0
The Role of Federated Learning in a Wireless World with Foundation Models—0
The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions—0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions—0
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges—0
Threats to Federated Learning: A Survey—0
TiFL: A Tier-based Federated Learning System—0
Tight Auditing of Differentially Private Machine Learning—0
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms—0
Tighter Performance Theory of FedExProx—0
Time-constrained Federated Learning (FL) in Push-Pull IoT Wireless Access—0
Time-Correlated Sparsification for Communication-Efficient Federated Learning—0
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning—0
Time-Distributed Backdoor Attacks on Federated Spiking Learning—0
Timely Asynchronous Hierarchical Federated Learning: Age of Convergence—0
Timely Communication in Federated Learning—0
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training—0
Time Minimization in Hierarchical Federated Learning—0
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence—0
Time-triggered Federated Learning over Wireless Networks—0
TinyReptile: TinyML with Federated Meta-Learning—0
Maximizing Global Model Appeal in Federated Learning—0
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data—0
Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning—0
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey—0
Topology-Based Reconstruction Prevention for Decentralised Learning—0
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks—0
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture—0
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices—0
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission—0
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices—0
Toward Communication Efficient Adaptive Gradient Method—0
Toward Data Heterogeneity of Federated Learning—0
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression—0
Toward efficient resource utilization at edge nodes in federated learning—0
Toward Malicious Clients Detection in Federated Learning—0
Towards Federated Graph Learning in One-shot Communication—0
Toward Responsible Federated Large Language Models: Leveraging a Safety Filter and Constitutional AI—0
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning—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