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 26012650 of 6771 papers

TitleStatusHype
Federated Large Language Models: Current Progress and Future Directions0
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions0
Federated Latent Class Regression for Hierarchical Data0
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving0
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders0
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications0
Federated Learning Aggregation: New Robust Algorithms with Guarantees0
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources0
Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis0
Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain0
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy0
Coordinated Replay Sample Selection for Continual Federated Learning0
Federated learning and next generation wireless communications: A survey on bidirectional relationship0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models0
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data0
Federated learning: Applications, challenges and future directions0
Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers0
Federated Learning Approach to Mitigate Water Wastage0
Federated Learning as a Mean-Field Game0
Federated Learning as a Network Effects Game0
Federated Learning: A Signal Processing Perspective0
Federated Learning Assisted Distributed Energy Optimization0
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence0
Federated Learning Attacks and Defenses: A Survey0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy0
Federated Learning-based Active Authentication on Mobile Devices0
Federated-Learning-Based Anomaly Detection for IoT Security Attacks0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems0
Federated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models0
Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids0
Federated Learning based Energy Demand Prediction with Clustered Aggregation0
Federated Learning based Hierarchical 3D Indoor Localization0
Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction0
Federated Learning-Based Localization with Heterogeneous Fingerprint Database0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization0
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning0
Cost-Effective Federated Learning Design0
Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks0
Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms0
Federated Learning-based Semantic Segmentation for Lane and Object Detection in Autonomous Driving0
FedCSD: A Federated Learning Based Approach for Code-Smell Detection0
Turning Federated Learning Systems Into Covert Channels0
Federated Learning Can Find Friends That Are Advantageous0
Federated Learning Challenges and Opportunities: An Outlook0
Covert Communication Based on the Poisoning Attack in Federated Learning0
Federated Learning Clients Clustering with Adaptation to Data Drifts0
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization0
Federated Learning Cost Disparity for IoT Devices0
Communication-Efficient Federated Learning Using Censored Heavy Ball Descent0
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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