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 3851–3900 of 6771 papers

TitleStatusHype
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model—0
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks—0
Text-driven Prompt Generation for Vision-Language Models in Federated Learning—0
DoCoFL: Downlink Compression for Cross-Device Federated Learning—0
DoStoVoQ: Doubly Stochastic Voronoi Vector Quantization SGD for Federated Learning—0
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus—0
The Applicability of Federated Learning to Official Statistics—0
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems—0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation—0
The Built-In Robustness of Decentralized Federated Averaging to Bad Data—0
The Copycat Perceptron: Smashing Barriers Through Collective Learning—0
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning—0
FedEGG: Federated Learning with Explicit Global Guidance—0
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective—0
The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting—0
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning—0
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning—0
The Future of Digital Health with Federated Learning—0
The Future of Large Language Model Pre-training is Federated—0
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning—0
Quality Inference in Federated Learning with Secure Aggregation—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
The Hidden Adversarial Vulnerabilities of Medical Federated Learning—0
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence—0
The Impact of Cut Layer Selection in Split Federated Learning—0
The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies—0
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning—0
The Key of Parameter Skew in Federated Learning—0
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning—0
The Need for Advanced Intelligence in NFV Management and Orchestration—0
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory—0
Theoretically Principled Federated Learning for Balancing Privacy and Utility—0
Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models—0
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
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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