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 37013750 of 6771 papers

TitleStatusHype
Can Decentralized Learning be more robust than Federated Learning?0
Client-specific Property Inference against Secure Aggregation in Federated LearningCode0
Learning to Backdoor Federated LearningCode0
Local Environment Poisoning Attacks on Federated Reinforcement Learning0
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoTCode0
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation0
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions0
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical TrainingCode4
Distributed Learning Meets 6G: A Communication and Computing Perspective0
Stochastic Clustered Federated Learning0
Mitigating Backdoors in Federated Learning with FLD0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
Federated Learning based Hierarchical 3D Indoor Localization0
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices0
Poster: Sponge ML Model Attacks of Mobile Apps0
Combating Exacerbated Heterogeneity for Robust Models in Federated LearningCode1
Differentially Private Distributed Convex Optimization0
Federated Covariate Shift Adaptation for Missing Target Output Values0
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic ForgettingCode1
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent0
Communication Trade-offs in Federated Learning of Spiking Neural Networks0
Towards Interpretable Federated Learning0
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning0
FedCLIP: Fast Generalization and Personalization for CLIP in Federated LearningCode2
Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions0
Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling0
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence0
Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge ComputingCode1
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups0
Better Generative Replay for Continual Federated Learning0
FedPDC:Federated Learning for Public Dataset Correction0
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue ClassificationCode0
Personalizing Federated Learning with Over-the-Air Computations0
Regulating Clients' Noise Adding in Federated Learning without Verification0
Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning0
Subspace based Federated Unlearning0
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated LearningCode0
FLINT: A Platform for Federated Learning Integration0
Coded Matrix Computations for D2D-enabled Linearized Federated Learning0
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space0
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model MarketCode0
Federated Nearest Neighbor Machine Translation0
FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis0
Personalized Decentralized Federated Learning with Knowledge Distillation0
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout0
Multi-Message Shuffled Privacy in Federated Learning0
Federated Radio Frequency Fingerprinting with Model Transfer and Adaptation0
Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks0
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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