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 1851–1900 of 6771 papers

TitleStatusHype
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints—0
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems—0
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data—0
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup—0
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments—0
Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices—0
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing—0
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition—0
Efficient Federated Learning with Timely Update Dissemination—0
Efficient Federated Split Learning for Large Language Models over Communication Networks—0
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation—0
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain—0
Efficient Fully Distributed Federated Learning with Adaptive Local Links—0
Efficient Image Representation Learning with Federated Sampled Softmax—0
Efficient Language Model Architectures for Differentially Private Federated Learning—0
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning—0
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization—0
Efficient Model Compression for Hierarchical Federated Learning—0
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation—0
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning—0
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks—0
Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation—0
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing—0
Efficient Privacy Preserving Edge Computing Framework for Image Classification—0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent—0
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT—0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning—0
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach—0
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout—0
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications—0
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing—0
Efficient Unbiased Sparsification—0
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates—0
Efficient Vertical Federated Learning with Secure Aggregation—0
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices—0
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology—0
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party—0
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks—0
Elastic Aggregation for Federated Optimization—0
Elastically-Constrained Meta-Learner for Federated Learning—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Accelerating Split Federated Learning over Wireless Communication Networks—0
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography—0
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense—0
Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs—0
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange—0
DABS: Data-Agnostic Backdoor attack at the Server in 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