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 23012350 of 6771 papers

TitleStatusHype
Distribution-Free Federated Learning with Conformal Predictions0
Distribution-Free Fair Federated Learning with Small Samples0
Fabricated Flips: Poisoning Federated Learning without Data0
Distribution-Aware Mobility-Assisted Decentralized Federated Learning0
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data0
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning0
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients0
A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization0
Distributionally Robust Federated Learning: An ADMM Algorithm0
Distributionally Robust Federated Learning with Client Drift Minimization0
Blind Federated Learning without initial model0
Distributionally Robust Federated Averaging0
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare0
Blind Federated Learning via Over-the-Air q-QAM0
Analysis of Total Variation Minimization for Clustered Federated Learning0
Decentralized Unsupervised Learning of Visual Representations0
Distributed U-net model and Image Segmentation for Lung Cancer Detection0
Distributed Trust Through the Lens of Software Architecture0
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms0
Analysis of regularized federated learning0
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach0
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents0
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization0
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy0
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning0
Distributed sequential federated learning0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning0
Binary Federated Learning with Client-Level Differential Privacy0
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression0
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks0
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization0
Distributed Optimization over Block-Cyclic Data0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence0
BICompFL: Stochastic Federated Learning with Bi-Directional Compression0
Distributed Online Optimization with Stochastic Agent Availability0
Distributed Online Learning with Multiple Kernels0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning0
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity0
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation0
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees0
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications0
Bias-Eliminating Augmentation Learning for Debiased Federated Learning0
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data0
Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics0
Distributed Machine Learning and the Semblance of Trust0
Biased Over-the-Air Federated Learning under Wireless Heterogeneity0
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network0
Distributed learning optimisation of Cox models can leak patient data: Risks and solutions0
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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