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 50515100 of 6771 papers

TitleStatusHype
Federated Learning Aggregation: New Robust Algorithms with Guarantees0
Robust Quantity-Aware Aggregation for Federated Learning0
Aligning Logits Generatively for Principled Black-Box Knowledge DistillationCode0
Kernel Normalized Convolutional NetworksCode0
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels0
E2FL: Equal and Equitable Federated Learning0
On the Decentralization of Blockchain-enabled Asynchronous Federated Learning0
Service Delay Minimization for Federated Learning over Mobile Devices0
Federated learning: Applications, challenges and future directions0
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles0
Providing Location Information at Edge Networks: A Federated Learning-Based Approach0
Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting0
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings0
Massive MIMO for Serving Federated Learning and Non-Federated Learning Users0
Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach0
On the (In)security of Peer-to-Peer Decentralized Machine LearningCode0
Federated Anomaly Detection over Distributed Data Streams0
FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs0
Tighter Regret Analysis and Optimization of Online Federated Learning0
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control0
Secure Aggregation for Federated Learning in Flower0
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference0
Blockchain-based Secure Client Selection in Federated Learning0
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural NetworksCode0
Residue-based Label Protection Mechanisms in Vertical Logistic Regression0
Protecting Data from all Parties: Combining FHE and DP in Federated Learning0
LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications0
Federated Multi-Armed Bandits Under Byzantine Attacks0
Deep Federated Anomaly Detection for Multivariate Time Series Data0
Federated Random Reshuffling with Compression and Variance Reduction0
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review0
Federated Learning with Noisy User Feedback0
Network Gradient Descent Algorithm for Decentralized Federated Learning0
Online Model Compression for Federated Learning with Large Models0
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray DataCode0
Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals0
Over-The-Air Federated Learning under Byzantine Attacks0
Can collaborative learning be private, robust and scalable?0
Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities0
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning0
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation0
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning0
Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates0
Training Mixed-Domain Translation Models via Federated Learning0
Privacy Amplification via Random Participation in Federated Learning0
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated LearningCode0
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction0
FedDKD: Federated Learning with Decentralized Knowledge Distillation0
Performance Weighting for Robust Federated Learning Against Corrupted Sources0
Intrinsic Gradient Compression for Scalable and 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