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 58515900 of 6771 papers

TitleStatusHype
A Vertical Federated Learning Framework for Graph Convolutional Network0
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective OptimizationCode0
Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective0
Compositional federated learning: Applications in distributionally robust averaging and meta learning0
Affect-driven Ordinal Engagement Measurement from Video0
Low-Latency Federated Learning over Wireless Channels with Differential Privacy0
Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning0
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning0
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning0
Zero-Shot Federated Learning with New Classes for Audio Classification0
A Vertical Federated Learning Framework for Horizontally Partitioned Labels0
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex LossesCode0
Quantized Federated Learning under Transmission Delay and Outage Constraints0
Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services0
Towards Heterogeneous Clients with Elastic Federated Learning0
Federated CycleGAN for Privacy-Preserving Image-to-Image Translation0
QuantumFed: A Federated Learning Framework for Collaborative Quantum Training0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions0
Over-the-Air Decentralized Federated Learning0
Privacy Assessment of Federated Learning using Private Personalized Layers0
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities0
On Large-Cohort Training for Federated Learning0
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning0
A Wasserstein Minimax Framework for Mixed Linear RegressionCode0
Decentralized Personalized Federated Learning for Min-Max Problems0
Dynamic Gradient Aggregation for Federated Domain Adaptation0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient0
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates0
Understanding the Interplay between Privacy and Robustness in Federated Learning0
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users0
Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning0
Federated Learning on Non-IID Data: A Survey0
Federated Learning with Buffered Asynchronous Aggregation0
Differentially Private Federated Learning via Inexact ADMM0
Vertical Federated Learning without Revealing Intersection Membership0
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigation0
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID DataCode0
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing0
FedNILM: Applying Federated Learning to NILM Applications at the Edge0
Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic EnvironmentsCode0
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning0
FedNL: Making Newton-Type Methods Applicable to Federated Learning0
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning0
Local Adaptivity in Federated Learning: Convergence and Consistency0
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare0
Federated Neural Collaborative Filtering0
Wireless Federated Learning with Limited Communication and Differential Privacy0
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for 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