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 351–400 of 6771 papers

TitleStatusHype
LLMs meet Federated Learning for Scalable and Secure IoT Management—0
Collaborative Split Federated Learning with Parallel Training and Aggregation—0
Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence—0
TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD DataCode0
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach—0
Bayesian Federated Learning for Continual Training—0
FedFetch: Faster Federated Learning with Adaptive Downstream PrefetchingCode0
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving—0
Efficient Federated Split Learning for Large Language Models over Communication Networks—0
GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning—0
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data—0
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models—0
Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling—0
SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework—0
VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture—0
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning—0
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning—0
Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients—0
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems—0
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum—0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning—0
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID GraphsCode0
Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification—0
Benchmarking Mutual Information-based Loss Functions in Federated Learning—0
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning—0
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data—0
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks—0
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
FedRecon: Missing Modality Reconstruction in Distributed Heterogeneous Environments—0
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders—0
Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks—0
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation—0
Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks—0
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding—0
Federated Learning with Layer Skipping: Efficient Training of Large Language Models for Healthcare NLP—0
Query-based Knowledge Transfer for Heterogeneous Learning Environments—0
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
Explainability and Continual Learning meet Federated Learning at the Network Edge—0
An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning—0
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data—0
The More is not the Merrier: Investigating the Effect of Client Size on Federated LearningCode0
Traversal Learning Coordination For Lossless And Efficient Distributed Learning—0
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities—0
FedMerge: Federated Personalization via Model Merging—0
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsCode0
Decentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis—0
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining—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