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 1901–1950 of 6771 papers

TitleStatusHype
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning—0
Elastic Aggregation for Federated Optimization—0
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy—0
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models—0
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party—0
EM for Mixture of Linear Regression with Clustered Data—0
Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect—0
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs—0
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI—0
Empirical Studies of Institutional Federated Learning For Natural Language Processing—0
An Expectation-Maximization Perspective on Federated Learning—0
Employing Layerwised Unsupervised Learning to Lessen Data and Loss Requirements in Forward-Forward Algorithms—0
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs—0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
Empowering Federated Learning for Massive Models with NVIDIA FLARE—0
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics—0
A collaborative ensemble construction method for federated random forest—0
Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning—0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection—0
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices—0
Efficient Vertical Federated Learning with Secure Aggregation—0
Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective—0
Enabling On-Device Training of Speech Recognition Models with Federated Dropout—0
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications—0
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data—0
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness—0
Efficient Unbiased Sparsification—0
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters—0
Encoded Spatial Attribute in Multi-Tier Federated Learning—0
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates—0
A new type of federated clustering: A non-model-sharing approach—0
End-to-End on-device Federated Learning: A case study—0
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing—0
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications—0
Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing—0
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout—0
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols—0
Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA—0
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks—0
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks—0
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning—0
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent—0
Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks—0
Energy Efficient Federated Learning Over Wireless Communication Networks—0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Byzantine-Robust Decentralized Federated Learning—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