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 62516300 of 6771 papers

TitleStatusHype
POSEIDON: Privacy-Preserving Federated Neural Network Learning0
Federated Edge Learning : Design Issues and Challenges0
SEEC: Semantic Vector Federation across Edge Computing Environments0
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model0
A Metamodel and Framework for AGI0
Collaborative Fairness in Federated LearningCode1
Performance Optimization for Federated Person Re-identification via Benchmark AnalysisCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Accelerating Federated Learning in Heterogeneous Data and Computational Environments0
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations0
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view TrainingCode0
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design0
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO0
Convergence of Federated Learning over a Noisy Downlink0
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning0
Federated Learning with Communication Delay in Edge Networks0
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy0
Toward Smart Security Enhancement of Federated Learning Networks0
Intelligent Radio Signal Processing: A Survey0
Shared MF: A privacy-preserving recommendation system0
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients0
Inverse Distance Aggregation for Federated Learning with Non-IID Data0
WAFFLE: Watermarking in Federated LearningCode1
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs0
FLBench: A Benchmark Suite for Federated Learning0
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank0
Siloed Federated Learning for Multi-Centric Histopathology Datasets0
Heterogeneous Federated Learning0
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data0
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data0
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning0
Privacy Preserving Vertical Federated Learning for Tree-based Models0
Addressing Class Imbalance in Federated LearningCode1
WAFFLe: Weight Anonymized Factorization for Federated Learning0
Distantly Supervised Relation Extraction in Federated SettingsCode1
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching0
FedNNNN: Norm-Normalized Neural Network Aggregation for Fast and Accurate Federated Learning0
Federated Learning via Synthetic Data0
Holdout SGD: Byzantine Tolerant Federated Learning0
Mime: Mimicking Centralized Stochastic Algorithms in Federated LearningCode1
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID DatasetsCode1
Federated Transfer Learning with Dynamic Gradient Aggregation0
Improving on-device speaker verification using federated learning with privacy0
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI0
State-of-the-art Techniques in Deep Edge Intelligence0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning0
The Need for Advanced Intelligence in NFV Management and Orchestration0
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization0
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy0
Communication-Efficient Federated Learning via Optimal Client Sampling0
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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