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 2151–2200 of 6771 papers

TitleStatusHype
Noise-Robust and Resource-Efficient ADMM-based Federated Learning—0
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue—0
On-Device Collaborative Language Modeling via a Mixture of Generalists and SpecialistsCode0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness—0
Green Federated Learning: A new era of Green Aware AI—0
FedAT: Federated Adversarial Training for Distributed Insider Threat Detection—0
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning—0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data—0
Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization—0
Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization—0
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed LearningCode0
Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots—0
Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach—0
TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based ClusteringCode0
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities—0
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity—0
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare—0
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks—0
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning—0
Enhancing Privacy in ControlNet and Stable Diffusion via Split Learning—0
Over-the-Air Federated Learning via Weighted Aggregation—0
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning—0
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
FedProphet: Memory-Efficient Federated Adversarial Training via Theoretic-Robustness and Low-Inconsistency Cascade Learning—0
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data—0
FedHide: Federated Learning by Hiding in the Neighbors—0
Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks—0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator—0
Federated X-armed Bandit with Flexible Personalisation—0
Federated Impression for Learning with Distributed Heterogeneous DataCode0
Riemannian Federated Learning via Averaging Gradient Stream—0
Personalized Federated Learning Techniques: Empirical Analysis—0
Rate-Constrained Quantization for Communication-Efficient Federated Learning—0
Contrastive Federated Learning with Tabular Data Silos—0
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning—0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency—0
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning—0
DynamicFL: Federated Learning with Dynamic Communication Resource AllocationCode0
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks—0
Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks—0
FedFT: Improving Communication Performance for Federated Learning with Frequency Space TransformationCode0
Unlocking the Potential of Model Calibration in Federated Learning—0
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography—0
Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression—0
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data—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