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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5051–5100 of 6771 papers

TitleStatusHype
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise—0
Accelerated Federated Learning with Decoupled Adaptive Optimization—0
Accelerated Gradient Descent Learning over Multiple Access Fading Channels—0
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization—0
Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems—0
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning—0
Accelerated Training of Federated Learning via Second-Order Methods—0
Accelerating Deep Learning with Fixed Time Budget—0
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation—0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization—0
Accelerating Fair Federated Learning: Adaptive Federated Adam—0
Accelerating Federated Edge Learning via Topology Optimization—0
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients—0
Accelerating Federated Learning in Heterogeneous Data and Computational Environments—0
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems—0
Accelerating Federated Learning via Momentum Gradient Descent—0
Accelerating Federated Split Learning via Local-Loss-Based Training—0
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers—0
Accelerating Hybrid Federated Learning Convergence under Partial Participation—0
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning—0
Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach—0
Accelerating Split Federated Learning over Wireless Communication Networks—0
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis—0
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Accessible Gesture-Driven Augmented Reality Interaction System—0
Accuracy and Privacy Evaluations of Collaborative Data Analysis—0
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping—0
Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)—0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning—0
A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets—0
Achieving Fairness Across Local and Global Models in Federated Learning—0
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization—0
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients—0
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients—0
Achieving Linear Speedup in Non-IID Federated Bilevel Learning—0
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning—0
Achieving Personalized Federated Learning with Sparse Local Models—0
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions—0
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points—0
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning—0
A Closer Look at Personalization in Federated Image Classification—0
A Coalition Formation Game Approach for Personalized Federated Learning—0
A collaborative ensemble construction method for federated random forest—0
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization—0
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret—0
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning—0
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified