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

Showing 16511700 of 6771 papers

TitleStatusHype
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing ModalitiesCode1
Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)Code0
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing0
Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility0
Feature Diversification and Adaptation for Federated Domain Generalization0
FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse LabelsCode0
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT ImagingCode0
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering0
Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning0
A Differentially Private Blockchain-Based Approach for Vertical Federated LearningCode0
Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems0
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory0
Stabilized Proximal-Point Methods for Federated OptimizationCode0
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges0
A Survey on LoRA of Large Language ModelsCode3
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion0
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients0
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated LearningCode0
Impact of Network Topology on Byzantine Resilience in Decentralized Federated LearningCode0
Synthetic Data Aided Federated Learning Using Foundation Models0
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning0
UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis0
Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape0
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning0
Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning0
MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained Machines to Collaboratively Train Larger Models0
Support Vector Based Anomaly Detection in Federated Learning0
FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning0
Enhanced Over-the-Air Federated Learning Using AI-based Fluid Antenna System0
Towards Federated RLHF with Aggregated Client Preference for LLMs0
Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks0
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation0
Joint Optimization of Resource Allocation and Data Selection for Fast and Cost-Efficient Federated Edge Learning0
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV0
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated LearningCode0
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks0
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight GenerationCode0
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis0
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation CompletenessCode0
Decentralized Intelligence Network (DIN)0
Enable the Right to be Forgotten with Federated Client Unlearning in Medical ImagingCode0
Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations0
FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection0
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models0
Show:102550
← PrevPage 34 of 136Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified