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 33013350 of 6771 papers

TitleStatusHype
Practical and General Backdoor Attacks against Vertical Federated Learning0
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation0
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning0
Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids0
Federated Few-shot LearningCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
Towards Quantum Federated Learning0
Fedstellar: A Platform for Decentralized Federated LearningCode1
Continual Adaptation of Vision Transformers for Federated LearningCode0
FedMultimodal: A Benchmark For Multimodal Federated LearningCode0
Towards Practical Federated Causal Structure LearningCode0
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems0
Privacy Risks in Reinforcement Learning for Household Robots0
An Efficient and Multi-private Key Secure Aggregation for Federated Learning0
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning0
A Client-server Deep Federated Learning for Cross-domain Surgical Image SegmentationCode0
Differentially Private Wireless Federated Learning Using Orthogonal Sequences0
Provably Personalized and Robust Federated LearningCode0
Federated Learning-based Vehicle Trajectory Prediction against CyberattacksCode1
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings0
(Amplified) Banded Matrix Factorization: A unified approach to private training0
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios0
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings0
Temporal Gradient Inversion Attacks with Robust Optimization0
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing SystemsCode0
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated LearningCode0
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning0
FedDec: Peer-to-peer Aided Federated Learning0
Personalized Graph Federated Learning with Differential Privacy0
Optimizing the Collaboration Structure in Cross-Silo Federated LearningCode1
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!Code0
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization0
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and ApplicationsCode0
Federated Learning for Medical Image Analysis: A Survey0
FedWon: Triumphing Multi-domain Federated Learning Without Normalization0
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers0
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems0
Federated Learning under Covariate Shifts with Generalization Guarantees0
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users0
A Systematic Literature Review on Client Selection in Federated Learning0
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift0
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMsCode0
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local UpdatesCode0
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering0
Fast Optimal Locally Private Mean Estimation via Random ProjectionsCode1
Phoenix: A Federated Generative Diffusion Model0
Personalization Disentanglement for Federated Learning: An explainable perspective0
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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