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 16511700 of 6771 papers

TitleStatusHype
Distributed Contrastive Learning for Medical Image Segmentation0
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing0
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation0
Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning0
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats0
Distributed Fixed Point Methods with Compressed Iterates0
Distributed Learning Approaches for Automated Chest X-Ray Diagnosis0
Distributed Learning for Time-varying Networks: A Scalable Design0
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes0
Distributed Learning for Wi-Fi AP Load Prediction0
Edge-cloud Collaborative Learning with Federated and Centralized Features0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges0
Distributed Learning Meets 6G: A Communication and Computing Perspective0
Distributed Learning on Heterogeneous Resource-Constrained Devices0
Distributed learning optimisation of Cox models can leak patient data: Risks and solutions0
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network0
Distributed Machine Learning and the Semblance of Trust0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics0
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications0
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees0
Data-driven geophysics: from dictionary learning to deep learning0
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation0
Distributed Online Learning with Multiple Kernels0
Distributed Online Optimization with Stochastic Agent Availability0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence0
Distributed Optimization over Block-Cyclic Data0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching0
Distributed sequential federated learning0
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression0
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy0
Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review0
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents0
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach0
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue0
Distributed Trust Through the Lens of Software Architecture0
Data, Competition, and Digital Platforms0
Decentralized Unsupervised Learning of Visual Representations0
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare0
Distributionally Robust Federated Averaging0
Distributionally Robust Federated Learning with Client Drift Minimization0
Distributionally Robust Federated Learning: An ADMM Algorithm0
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration0
Distribution-Aware Mobility-Assisted Decentralized Federated Learning0
Distribution-Free Fair Federated Learning with Small Samples0
Distribution-Free Federated Learning with Conformal Predictions0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity0
Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach0
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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