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 34513500 of 6771 papers

TitleStatusHype
Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchain0
A novel parameter decoupling approach of personalized federated learning for image analysis0
Adaptive Federated Pruning in Hierarchical Wireless Networks0
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning0
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning0
Federated Learning over Harmonized Data Silos0
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments0
A Survey of Federated Evaluation in Federated Learning0
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling0
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous DataCode0
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction0
GPFedRec: Graph-guided Personalization for Federated RecommendationCode1
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus0
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning0
Multi-Tier Client Selection for Mobile Federated Learning Networks0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems0
Self-Supervised Federated Learning for Fast MR Imaging0
Securing Distributed SGD against Gradient Leakage ThreatsCode0
Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference0
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
XTab: Cross-table Pretraining for Tabular TransformersCode1
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation0
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications0
Towards Building the Federated GPT: Federated Instruction TuningCode2
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise HeterogeneityCode1
Flame: Simplifying Topology Extension in Federated LearningCode1
Collaborative Chinese Text Recognition with Personalized Federated Learning0
Semi-Supervised Federated Learning for Keyword SpottingCode0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation0
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning0
Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services0
Turning Privacy-preserving Mechanisms against Federated Learning0
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts0
FedHB: Hierarchical Bayesian Federated Learning0
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof0
Federated Learning in Wireless Networks via Over-the-Air Computations0
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey0
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion0
MrTF: Model Refinery for Transductive Federated LearningCode0
Gradient Leakage Defense with Key-Lock Module for Federated LearningCode0
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model0
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth OrbitCode0
Now It Sounds Like You: Learning Personalized Vocabulary On Device0
FedNC: A Secure and Efficient Federated Learning Method with Network Coding0
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval0
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets0
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization0
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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