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 36013650 of 6771 papers

TitleStatusHype
Securing Federated Learning against Overwhelming Collusive Attackers0
Securing Federated Learning against Backdoor Threats with Foundation Model Integration0
Securing Genomic Data Against Inference Attacks in Federated Learning Environments0
Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework0
Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning0
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution0
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning0
Security Analysis of SplitFed Learning0
Security and Privacy for Artificial Intelligence: Opportunities and Challenges0
Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare0
Security and Privacy Issues of Federated Learning0
Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing0
Security and Privacy Preserving Deep Learning0
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance0
SEEC: Semantic Vector Federation across Edge Computing Environments0
See through Gradients: Image Batch Recovery via GradInversion0
Segmented Federated Learning for Adaptive Intrusion Detection System0
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations0
Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity0
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks0
Self-Aware Personalized Federated Learning0
Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services0
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT0
Self-supervised Cross-silo Federated Neural Architecture Search0
Self-Supervised Federated Learning for Fast MR Imaging0
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management0
SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks0
Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems0
Semi-Cyclic Stochastic Gradient Descent0
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data0
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity0
Semi-Decentralized Federated Learning with Collaborative Relaying0
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework0
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling0
VFed-SSD: Towards Practical Vertical Federated Advertising0
Semi-supervised Federated Learning for Activity Recognition0
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design0
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings0
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks0
Semi-Variance Reduction for Fair Federated Learning0
Sensing and Mapping for Better Roads: Initial Plan for Using Federated Learning and Implementing a Digital Twin to Identify the Road Conditions in a Developing Country -- Sri Lanka0
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning0
Separation of Powers in Federated Learning0
Sequence-level self-learning with multiple hypotheses0
Sequential Compression Layers for Efficient Federated Learning in Foundational Models0
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets0
Server Averaging for Federated Learning0
Decentralized Federated Learning for UAV Networks: Architecture, 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