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 40014050 of 6771 papers

TitleStatusHype
Project Florida: Federated Learning Made Easy0
Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering0
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model0
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications0
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain0
Proof-of-Data: A Consensus Protocol for Collaborative Intelligence0
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning0
Proof of Swarm Based Ensemble Learning for Federated Learning Applications0
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases0
Protea: Client Profiling within Federated Systems using Flower0
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation0
Protecting Data from all Parties: Combining FHE and DP in Federated Learning0
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning0
Protection Against Reconstruction and Its Applications in Private Federated Learning0
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation0
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data0
Prototype-Based Layered Federated Cross-Modal Hashing0
Prototype Guided Federated Learning of Visual Feature Representations0
Prototype Helps Federated Learning: Towards Faster Convergence0
Prototype of deployment of Federated Learning with IoT devices0
Provable Federated Adversarial Learning via Min-max Optimization0
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains0
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization0
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation0
Fair Federated Learning via Bounded Group Loss0
Provably Secure Federated Learning against Malicious Clients0
Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework0
Providing Location Information at Edge Networks: A Federated Learning-Based Approach0
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains0
Proximity-based Self-Federated Learning0
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!0
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning0
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images0
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy0
Public Data-Assisted Mirror Descent for Private Model Training0
QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality0
QFAL: Quantum Federated Adversarial Learning0
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection0
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning0
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution0
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning0
Communication-Efficient Federated Learning With Data and Client Heterogeneity0
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation0
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations0
Quality monitoring of federated Covid-19 lesion segmentation0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning0
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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