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 40014050 of 6771 papers

TitleStatusHype
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training0
Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Securing Distributed SGD against Gradient Leakage ThreatsCode0
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature0
Self-Supervised Federated Learning for Fast MR Imaging0
Semi-Supervised Federated Learning for Keyword SpottingCode0
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning0
Turning Privacy-preserving Mechanisms against Federated Learning0
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications0
Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services0
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation0
Collaborative Chinese Text Recognition with Personalized Federated Learning0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation0
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts0
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey0
Federated Learning in Wireless Networks via Over-the-Air Computations0
FedHB: Hierarchical Bayesian Federated Learning0
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof0
MrTF: Model Refinery for Transductive Federated LearningCode0
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion0
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth OrbitCode0
Gradient Leakage Defense with Key-Lock Module for Federated LearningCode0
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model0
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
Can Fair Federated Learning reduce the need for Personalisation?0
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization0
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training0
A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning0
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning0
Federated Neural Radiance FieldsCode0
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning0
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures OptimizerCode0
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier AnchoringCode0
DepthFL: Depthwise Federated Learning for Heterogeneous Clients0
Scalable Data Point Valuation in Decentralized LearningCode0
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks0
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning0
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients InspectionCode0
Hyperparameter Optimization through Neural Network Partitioning0
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations0
Reveal Your Images: Gradient Leakage Attack against Unbiased Sampling-Based Secure AggregationCode0
Hierarchical and Decentralised Federated Learning0
Client Recruitment for Federated Learning in ICU Length of Stay PredictionCode0
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning0
Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) FrameworkCode0
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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