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 28512900 of 6771 papers

TitleStatusHype
Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence0
Federated Learning with Nonvacuous Generalisation Bounds0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning0
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks0
Over-the-Air Federated Learning and Optimization0
Passive Inference Attacks on Split Learning via Adversarial RegularizationCode1
Federated Learning with Convex Global and Local ConstraintsCode0
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language ModelsCode2
VFLAIR: A Research Library and Benchmark for Vertical Federated LearningCode1
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
FLrce: Resource-Efficient Federated Learning with Early-Stopping StrategyCode0
Federated Reinforcement Learning for Resource Allocation in V2X Networks0
Federated Multi-Objective Learning0
Multimodal Federated Learning in Healthcare: a Review0
A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization0
Tackling Heterogeneity in Medical Federated learning via Vision Transformers0
PAGE: Equilibrate Personalization and Generalization in Federated Learning0
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning0
Federated Class-Incremental Learning with Prompting0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding0
Price of Stability in Quality-Aware Federated Learning0
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection0
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks0
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning0
FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning0
The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies0
Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT SensingCode1
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients0
RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning0
Federated Quantum Machine Learning with Differential Privacy0
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication0
Secure Decentralized Learning with Blockchain0
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning0
Federated Learning with Reduced Information Leakage and ComputationCode0
Differentially Private Multi-Site Treatment Effect Estimation0
Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory0
Exploring adversarial attacks in federated learning for medical imaging0
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks0
Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning0
Text-driven Prompt Generation for Vision-Language Models in Federated Learning0
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning0
FedFed: Feature Distillation against Data Heterogeneity in Federated LearningCode1
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization0
Asymmetrically Decentralized Federated Learning0
Towards Scalable Wireless Federated Learning: Challenges and Solutions0
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications0
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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