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 551–600 of 6771 papers

TitleStatusHype
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning—0
Detecting Backdoor Attacks in Federated Learning via Direction Alignment InspectionCode1
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative ModelsCode0
Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing—0
Right Reward Right Time for Federated Learning—0
FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA Subparameter Updates—0
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data—0
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-UpCode0
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model—0
Capture Global Feature Statistics for One-Shot Federated LearningCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability—0
Federated Learning in NTNs: Design, Architecture and Challenges—0
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges—0
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated LearningCode1
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Privacy Protection in Prosumer Energy Management Based on Federated Learning—0
Experimental Demonstration of Over the Air Federated Learning for Cellular Networks—0
Federated Learning for Diffusion Models—0
Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks—0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation—0
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning—0
Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty—0
Secure On-Device Video OOD Detection Without BackpropagationCode1
Biased Federated Learning under Wireless Heterogeneity—0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security—0
NoT: Federated Unlearning via Weight Negation—0
Personalized Federated Learning via Learning Dynamic Graphs—0
FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User DataCode1
Uncertainty-Aware Explainable Federated Learning—0
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting—0
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence—0
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11—0
One-Shot Clustering for Federated Learning—0
Subgraph Federated Learning for Local GeneralizationCode1
FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting—0
Generalization in Federated Learning: A Conditional Mutual Information Framework—0
InFL-UX: A Toolkit for Web-Based Interactive Federated LearningCode0
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge—0
Controlled privacy leakage propagation throughout overlapping grouped learning—0
Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association—0
Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings—0
FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint VerificationCode0
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion—0
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models—0
Towards Trustworthy Federated Learning—0
Convergence Analysis of Federated Learning Methods Using Backward Error Analysis—0
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data—0
Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs—0
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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