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 551600 of 6771 papers

TitleStatusHype
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning0
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 Auditing0
Right Reward Right Time for Federated Learning0
FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA Subparameter Updates0
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data0
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-UpCode0
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model0
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 Auditability0
Federated Learning in NTNs: Design, Architecture and Challenges0
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges0
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated LearningCode1
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast0
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 Learning0
Experimental Demonstration of Over the Air Federated Learning for Cellular Networks0
Federated Learning for Diffusion Models0
Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation0
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning0
Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty0
Secure On-Device Video OOD Detection Without BackpropagationCode1
Biased Federated Learning under Wireless Heterogeneity0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security0
NoT: Federated Unlearning via Weight Negation0
Personalized Federated Learning via Learning Dynamic Graphs0
FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User DataCode1
Uncertainty-Aware Explainable Federated Learning0
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting0
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence0
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv110
One-Shot Clustering for Federated Learning0
Subgraph Federated Learning for Local GeneralizationCode1
FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting0
Generalization in Federated Learning: A Conditional Mutual Information Framework0
InFL-UX: A Toolkit for Web-Based Interactive Federated LearningCode0
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge0
Controlled privacy leakage propagation throughout overlapping grouped learning0
Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association0
Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings0
FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint VerificationCode0
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion0
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models0
Towards Trustworthy Federated Learning0
Convergence Analysis of Federated Learning Methods Using Backward Error Analysis0
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data0
Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs0
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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