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 48514900 of 6771 papers

TitleStatusHype
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR0
Fast Composite Optimization and Statistical Recovery in Federated Learning0
Collaborative Learning in Kernel-based Bandits for Distributed Users0
Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees0
Suppressing Poisoning Attacks on Federated Learning for Medical ImagingCode0
Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations0
Accelerated Federated Learning with Decoupled Adaptive Optimization0
Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation0
Enhanced Security and Privacy via Fragmented Federated LearningCode0
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent KernelsCode0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis0
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval0
Efficient and Privacy Preserving Group Signature for Federated Learning0
Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX0
Mechanisms that Incentivize Data Sharing in Federated Learning0
FedSS: Federated Learning with Smart Selection of clients0
Smart Multi-tenant Federated Learning0
Multi-Model Federated Learning with Provable Guarantees0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningCode0
The Poisson binomial mechanism for secure and private federated learning0
A Survey on Participant Selection for Federated Learning in Mobile Networks0
StatMix: Data augmentation method that relies on image statistics in federated learning0
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox0
Decentralized digital twins of complex dynamical systems0
FedHeN: Federated Learning in Heterogeneous Networks0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data0
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms0
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy0
Defending against the Label-flipping Attack in Federated Learning0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control0
Protea: Client Profiling within Federated Systems using Flower0
Backdoor Attack is a Devil in Federated GAN-based Medical Image SynthesisCode0
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation0
FL-Defender: Combating Targeted Attacks in Federated LearningCode0
Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks0
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks0
BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models0
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning0
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity0
Towards Federated Long-Tailed Learning0
Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach0
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning0
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images0
Cross-domain Federated Object Detection0
DP^2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring0
AFAFed -- Protocol analysis0
Split Two-Tower Model for Efficient and Privacy-Preserving Cross-device Federated Recommendation0
Secure Forward Aggregation for Vertical Federated Neural Networks0
Differentially Private Federated Combinatorial Bandits with Constraints0
Cross-Silo Federated Learning: Challenges and Opportunities0
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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