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 39013950 of 6771 papers

TitleStatusHype
Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare0
Network Adaptive Federated Learning: Congestion and Lossy Compression0
Federated Learning under Heterogeneous and Correlated Client AvailabilityCode1
FedDebug: Systematic Debugging for Federated Learning ApplicationsCode0
Federated Learning for Energy Constrained IoT devices: A systematic mapping study0
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation0
Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems0
Why Batch Normalization Damage Federated Learning on Non-IID Data?Code0
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices0
IronForge: An Open, Secure, Fair, Decentralized Federated LearningCode0
Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks0
Single-round Self-supervised Distributed Learning using Vision Transformer0
Federated Learning for Data StreamsCode0
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems0
Machine Learning for Large-Scale Optimization in 6G Wireless Networks0
Recent Advances on Federated Learning: A Systematic Survey0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics0
SPEFL: Efficient Security and Privacy Enhanced Federated Learning Against Poisoning Attacks0
Fairness and Effectiveness in Federated Learning on Non-independent and Identically Distributed Data0
Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation0
Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization0
FedPD: Federated Open Set Recognition with Parameter Disentanglement0
Generative Gradient Inversion via Over-Parameterized Networks in Federated LearningCode0
Robust Heterogeneous Federated Learning under Data CorruptionCode0
Personalized Semantics Excitation for Federated Image Classification0
Global Balanced Experts for Federated Long-Tailed LearningCode0
Bias-Eliminating Augmentation Learning for Debiased Federated Learning0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?0
Reliable and Interpretable Personalized Federated Learning0
Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity0
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation0
CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation LossCode0
Rethinking Federated Learning With Domain Shift: A Prototype ViewCode1
Elastic Aggregation for Federated Optimization0
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous ClientsCode1
Mutual Information Regularization for Vertical Federated Learning0
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning FrameworkCode0
Efficient On-device Training via Gradient FilteringCode1
FedICT: Federated Multi-task Distillation for Multi-access Edge ComputingCode0
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal EffectsCode0
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling0
Graph Federated Learning for CIoT Devices in Smart Home ApplicationsCode0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management0
Characterization of the Global Bias Problem in Aerial Federated Learning0
Proof of Swarm Based Ensemble Learning for Federated Learning Applications0
CC-FedAvg: Computationally Customized Federated Averaging0
A Survey on Federated Recommendation Systems0
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions0
Democratising Knowledge Representation with BioCypher0
Social-Aware Clustered Federated Learning with Customized Privacy Preservation0
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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