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 801850 of 6771 papers

TitleStatusHype
Sparse Incremental Aggregation in Satellite Federated Learning0
The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions0
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital CollaborationCode1
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching0
Federated Learning with Sample-level Client Drift Mitigation0
Trustformer: A Trusted Federated Transformer0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks0
Synesthesia of Machines (SoM)-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data0
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning0
Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks0
Temporal Analysis of Adversarial Attacks in Federated Learning0
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup0
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications0
UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning0
Multi-Task Over-the-Air Federated Learning in Cell-Free Massive MIMO Systems0
Client-Centric Federated Adaptive Optimization0
ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems0
HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning0
pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data0
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning0
Artificial Intelligence-Driven Clinical Decision Support Systems0
GLow -- A Novel, Flower-Based Simulated Gossip Learning StrategyCode0
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes0
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication0
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning0
UFGraphFR: An attempt at a federated recommendation system based on user text characteristicsCode0
Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design, and Future DirectionsCode1
A Federated Deep Learning Framework for Cell-Free RSMA Networks0
Reliable Imputed-Sample Assisted Vertical Federated Learning0
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
STHFL: Spatio-Temporal Heterogeneous Federated Learning0
Encoded Spatial Attribute in Multi-Tier Federated Learning0
Aggregating Low Rank Adapters in Federated Fine-tuning0
Collaborative Content Moderation in the Fediverse0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning0
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball ComputingCode0
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning0
AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated LearningCode0
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning0
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning0
Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning0
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis0
Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning0
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions0
FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot DetectionCode0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems0
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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