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 23512400 of 6771 papers

TitleStatusHype
Biased Federated Learning under Wireless Heterogeneity0
Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning0
Distributed Learning on Heterogeneous Resource-Constrained Devices0
Distributed Learning Meets 6G: A Communication and Computing Perspective0
BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges0
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction0
BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse0
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach0
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning0
Achieving Fairness Across Local and Global Models in Federated Learning0
A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus0
FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication0
Distributed Learning for Wi-Fi AP Load Prediction0
Distributed Learning for UAV Swarms0
Distributed Learning for Time-varying Networks: A Scalable Design0
Distributed Learning Approaches for Automated Chest X-Ray Diagnosis0
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients0
An Agnostic Approach to Federated Learning with Class Imbalance0
Distributed Fixed Point Methods with Compressed Iterates0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling0
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection0
Distributed Networked Learning with Correlated Data0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration0
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity0
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging0
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing0
Distributed Contrastive Learning for Medical Image Segmentation0
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning0
Distributed Continual Learning0
Distributed, communication-efficient, and differentially private estimation of KL divergence0
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks0
Distributed collaborative anomalous sound detection by embedding sharing0
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning0
An advanced data fabric architecture leveraging homomorphic encryption and federated learning0
Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning0
Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles0
Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework0
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems0
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks0
BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers0
An Adaptive Differential Privacy Method Based on Federated Learning0
Distilling A Universal Expert from Clustered Federated Learning0
Distilled One-Shot Federated Learning0
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity0
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data0
Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction0
An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning0
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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