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 24512500 of 6771 papers

TitleStatusHype
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning0
Task-Agnostic Federated Learning0
Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networks0
Maze Discovery using Multiple Robots via Federated Learning0
Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework0
Achieving Fairness Across Local and Global Models in Federated Learning0
Personalized federated learning based on feature fusion0
Federated Transfer Learning Aided Interference Classification in GNSS Signals0
Semi-Variance Reduction for Fair Federated Learning0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning0
Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy0
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things0
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection0
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training0
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning ApproachCode0
Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic Linear Combination0
Supersonic OT: Fast Unconditionally Secure Oblivious Transfer0
Rate-Splitting Multiple Access for Overloaded Multi-group Multicast: A First Experimental Study0
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach0
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated LearningCode0
Communication-efficient Vertical Federated Learning via Compressed Error FeedbackCode0
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization0
CollaFuse: Collaborative Diffusion ModelsCode0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks0
Bayes' capacity as a measure for reconstruction attacks in federated learning0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments0
Federated Learning with a Single Shared ImageCode0
Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing0
Synergizing Foundation Models and Federated Learning: A SurveyCode0
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty EstimationCode0
UIFV: Data Reconstruction Attack in Vertical Federated Learning0
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning0
Federated Learning with Limited Node Labels0
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation0
Training Diffusion Models with Federated Learning0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs0
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification0
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions0
Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks0
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality0
When NOMA Meets AIGC: Enhanced Wireless Federated Learning0
Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA0
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models0
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data0
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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