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 30513100 of 6771 papers

TitleStatusHype
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests0
Federated Object Detection for Quality Inspection in Shared Production0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices0
Federated One-Shot Learning with Data Privacy and Objective-Hiding0
Federated Online Adaptation for Deep Stereo0
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data0
A review of federated learning in renewable energy applications: Potential, challenges, and future directions0
Delay Optimization of a Federated Learning-based UAV-aided IoT network0
Federated Optimization of Smooth Loss Functions0
Federated Optimization with Doubly Regularized Drift Correction0
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning0
Delay-Tolerant Local SGD for Efficient Distributed Training0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning0
Probably Approximately Correct Federated Learning0
Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm0
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy0
Democratising Knowledge Representation with BioCypher0
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction0
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System0
Democratizing AI in Africa: FL for Low-Resource Edge Devices0
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler0
A Crowdsourcing Framework for On-Device Federated Learning0
Federated Quantum Long Short-term Memory (FedQLSTM)0
Federated Quantum Machine Learning0
Federated Quantum Machine Learning with Differential Privacy0
Federated Quantum Natural Gradient Descent for Quantum Federated Learning0
Federated Radio Frequency Fingerprinting with Model Transfer and Adaptation0
Federated Recommendation System via Differential Privacy0
Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data0
Federated Unsupervised Domain Adaptation for Face Recognition0
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms0
Federated Reinforcement Learning for Resource Allocation in V2X Networks0
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges0
Federated Remote Physiological Measurement with Imperfect Data0
Federated Representation Learning for Automatic Speech Recognition0
Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent0
Federated Representation Learning via Maximal Coding Rate Reduction0
Federated Residual Learning0
Federated Retrieval-Augmented Generation: A Systematic Mapping Study0
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning0
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity0
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity0
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning0
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning0
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning0
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation0
Federated Self-Supervised Learning for Acoustic Event Classification0
A Federated Approach to Predict Emojis in Hindi Tweets0
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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