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 31013150 of 6771 papers

TitleStatusHype
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems0
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning0
Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach0
Declarative Privacy-Preserving Inference Queries0
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy ClientsCode0
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning0
Attention on Personalized Clinical Decision Support System: Federated Learning ApproachCode0
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity0
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting0
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces0
Differentially-Private Multi-Tier Federated Learning0
FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph EnhancementCode0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks0
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection0
Improving Local Training in Federated Learning via Temperature Scaling0
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETsCode0
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks0
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities0
Technical Report: On the Convergence of Gossip Learning in the Presence of Node Inaccessibility0
Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning0
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed DataCode0
Federated Unlearning for Human Activity Recognition0
Learn What You Need in Personalized Federated LearningCode0
Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare0
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing0
Over-the-Air Federated Learning with Phase Noise: Analysis and Countermeasures0
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach0
ADVENT: Attack/Anomaly Detection in VANETs0
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation0
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning0
Formal Logic Enabled Personalized Federated Learning Through Property Inference0
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks0
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality0
Joint Probability Selection and Power Allocation for Federated Learning0
Efficient Wireless Federated Learning via Low-Rank Gradient FactorizationCode0
Vertical Federated Image Segmentation0
The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting0
Gradient Coreset for Federated LearningCode0
FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions0
Intelligent Data-Driven Architectural Features Orchestration for Network Slicing0
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation0
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning0
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks0
Congruent Learning for Self-Regulated Federated Learning in 6GCode0
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction0
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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