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 13011350 of 6771 papers

TitleStatusHype
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers0
Data science and AI in FinTech: An overview0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection0
Backdoor Attacks on Federated Meta-Learning0
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions0
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems0
Backdoor Attacks in Peer-to-Peer Federated Learning0
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection0
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling0
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions0
AIDRIN 2.0: A Framework to Assess Data Readiness for AI0
Adaptive Federated Pruning in Hierarchical Wireless Networks0
Accuracy and Privacy Evaluations of Collaborative Data Analysis0
Backdoor attacks and defenses in feature-partitioned collaborative learning0
Backdoor Attack on Vertical Federated Graph Neural Network Learning0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information0
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis0
AI-based traffic analysis in digital twin networks0
A Bayesian Federated Learning Framework with Online Laplace Approximation0
Information-Geometric Barycenters for Bayesian Federated Learning0
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction0
AI-Based Crypto Tokens: The Illusion of Decentralized AI?0
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity0
AI Approaches in Processing and Using Data in Personalized Medicine0
Adaptive Federated Minimax Optimization with Lower Complexities0
Decentralized Personalized Federated Learning0
Decentralized Personalized Federated Learning for Min-Max Problems0
Decoding FL Defenses: Systemization, Pitfalls, and Remedies0
A Web-Based Solution for Federated Learning with LLM-Based Automation0
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments0
Adaptive Federated Learning via New Entropy Approach0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data0
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations0
Self-supervised audio representation learning for mobile devices0
A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS0
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring0
A Hybrid Federated Kernel Regularized Least Squares Algorithm0
A Vertical Federated Learning Framework for Graph Convolutional Network0
A Vertical Federated Learning Framework for Horizontally Partitioned Labels0
Large-Scale Secure XGB for Vertical Federated Learning0
Decentralized Low-Rank Fine-Tuning of Large Language Models0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control0
Adaptive Federated Learning Over the Air0
Decentralized Health Intelligence Network (DHIN)0
Auxo: Efficient Federated Learning via Scalable Client Clustering0
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning0
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning0
Decentralized Intelligence Network (DIN)0
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning0
Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective0
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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