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 2401–2450 of 6771 papers

TitleStatusHype
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges—0
Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems—0
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain—0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion—0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—0
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients—0
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated LearningCode0
Synthetic Data Aided Federated Learning Using Foundation Models—0
Impact of Network Topology on Byzantine Resilience in Decentralized Federated LearningCode0
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning—0
UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis—0
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning—0
Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape—0
Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning—0
Support Vector Based Anomaly Detection in Federated Learning—0
FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning—0
MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained Machines to Collaboratively Train Larger Models—0
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks—0
Enhanced Over-the-Air Federated Learning Using AI-based Fluid Antenna System—0
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight GenerationCode0
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated LearningCode0
Joint Optimization of Resource Allocation and Data Selection for Fast and Cost-Efficient Federated Edge Learning—0
Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks—0
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV—0
Towards Federated RLHF with Aggregated Client Preference for LLMs—0
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation—0
Enable the Right to be Forgotten with Federated Client Unlearning in Medical ImagingCode0
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation CompletenessCode0
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point—0
Decentralized Intelligence Network (DIN)—0
Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
Federated Binary Matrix Factorization using Proximal Optimization—0
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection—0
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated LearningCode0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
Personalized Interpretation on Federated Learning: A Virtual Concepts approach—0
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning—0
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation—0
Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation—0
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design—0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling—0
QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated LearningCode0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization—0
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap—0
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced 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