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 64016450 of 6771 papers

TitleStatusHype
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges0
Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation0
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference0
eFedLLM: Efficient LLM Inference Based on Federated Learning0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective0
Effective and Efficient Federated Tree Learning on Hybrid Data0
Effective and secure federated online learning to rank0
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data0
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach0
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks0
EFFGAN: Ensembles of fine-tuned federated GANs0
Efficient Adaptive Federated Optimization0
Efficient Adaptive Federated Optimization of Federated Learning for IoT0
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout0
Efficient and Privacy Preserving Group Signature for Federated Learning0
Efficient and Private Federated Learning with Partially Trainable Networks0
Efficient and Reliable Overlay Networks for Decentralized Federated Learning0
Efficient and Secure Federated Learning for Financial Applications0
Efficient Asynchronous Federated Learning with Sparsification and Quantization0
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost0
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning0
Efficient Client Contribution Evaluation for Horizontal Federated Learning0
Efficient Client Selection in Federated Learning0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach0
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems0
Efficient Conformal Prediction under Data Heterogeneity0
Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning0
Efficient Cross-Domain Federated Learning by MixStyle Approximation0
Efficient Data Distribution Estimation for Accelerated Federated Learning0
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach0
Efficient Deployment of Large Language Models on Resource-constrained Devices0
Efficient Device Scheduling with Multi-Job Federated Learning0
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors0
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices0
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation0
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints0
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data0
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup0
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout0
Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices0
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing0
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition0
Efficient Federated Learning with Timely Update Dissemination0
Efficient Federated Split Learning for Large Language Models over Communication Networks0
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation0
Efficient Fully Distributed Federated Learning with Adaptive Local Links0
Efficient Image Representation Learning with Federated Sampled Softmax0
Efficient Language Model Architectures for Differentially Private Federated Learning0
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning0
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization0
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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