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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5251–5300 of 6771 papers

TitleStatusHype
AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models—0
AFLGuard: Byzantine-robust Asynchronous Federated Learning—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning—0
A Framework for Exploring Federated Community Detection—0
A Framework for Incentivized Collaborative Learning—0
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy—0
A Game-theoretic Framework for Privacy-preserving Federated Learning—0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning—0
Age Aware Scheduling for Differentially-Private Federated Learning—0
Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning—0
Heterogeneous Federated Learning with Splited Language Model—0
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees—0
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates—0
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning—0
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy—0
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning—0
Age-of-Gradient Updates for Federated Learning over Random Access Channels—0
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach—0
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration—0
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes—0
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices—0
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning—0
Aggregating Low Rank Adapters in Federated Fine-tuning—0
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization—0
Aggregation Weighting of Federated Learning via Generalization Bound Estimation—0
AGIC: Approximate Gradient Inversion Attack on Federated Learning—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification—0
A Graph Federated Architecture with Privacy Preserving Learning—0
A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach—0
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs—0
A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training—0
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning—0
Large-Scale Secure XGB for Vertical Federated Learning—0
A Hybrid Federated Kernel Regularized Least Squares Algorithm—0
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments—0
AI Approaches in Processing and Using Data in Personalized Medicine—0
AI-Based Crypto Tokens: The Illusion of Decentralized AI?—0
AI-based traffic analysis in digital twin networks—0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information—0
AIDRIN 2.0: A Framework to Assess Data Readiness for AI—0
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection—0
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions—0
Data science and AI in FinTech: An overview—0
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory—0
AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
A Joint Gradient and Loss Based Clustered Federated Learning Design—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

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