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 54515500 of 6771 papers

TitleStatusHype
Parameterizing Federated Continual Learning for Reproducible Research0
Gradient Correction in Federated Learning with Adaptive Optimization0
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models0
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization0
Partial Federated Learning0
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity0
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits0
Partial Variable Training for Efficient On-Device Federated Learning0
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services0
Partitioned Variational Inference: A unified framework encompassing federated and continual learning0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed0
PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation0
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy0
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance0
PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Models0
Peer-to-Peer Deep Learning for Beyond-5G IoT0
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition0
Peer-to-peer Federated Learning on Graphs0
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning0
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning0
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning0
Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training0
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization0
Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks0
Performance Analysis of Decentralized Federated Learning Deployments0
Federated Learning with Differential Privacy: Algorithms and Performance Analysis0
Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview0
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks0
Performance Weighting for Robust Federated Learning Against Corrupted Sources0
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts0
PersA-FL: Personalized Asynchronous Federated Learning0
Personalised Federated Learning: A Combinational Approach0
Personalised Federated Learning On Heterogeneous Feature Spaces0
Personalization Disentanglement for Federated Learning: An explainable perspective0
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation0
Personalized Decentralized Federated Learning with Knowledge Distillation0
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs0
Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning0
Personalized Federated Learning: A Meta-Learning Approach0
Personalized Cross-Silo Federated Learning on Non-IID Data0
Personalized federated learning based on feature fusion0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework0
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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