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 55515600 of 6771 papers

TitleStatusHype
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning0
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives0
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning0
PFedDST: Personalized Federated Learning with Decentralized Selection Training0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing0
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning0
pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology0
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning0
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization0
pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data0
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation0
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning0
Phoenix: A Federated Generative Diffusion Model0
Photon: Federated LLM Pre-Training0
Picking Daisies in Private: Federated Learning from Small Datasets0
IP-FL: Incentivized and Personalized Federated Learning0
PiPar: Pipeline Parallelism for Collaborative Machine Learning0
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion0
PIVODL: Privacy-preserving vertical federated learning over distributed labels0
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification0
PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning0
PLMM: Personal Large Language Models on Mobile Devices0
PluralLLM: Pluralistic Alignment in LLMs via Federated Learning0
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup0
PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning0
Poisoning Attacks and Defenses in Federated Learning: A Survey0
Poisoning Attacks and Defenses to Federated Unlearning0
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study0
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction0
Poisoning Attacks on Federated Learning for Autonomous Driving0
Poisoning Federated Recommender Systems with Fake Users0
Poisoning with A Pill: Circumventing Detection in Federated Learning0
PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning0
Population Expansion for Training Language Models with Private Federated Learning0
Population Normalization for Federated Learning0
POSEIDON: Privacy-Preserving Federated Neural Network Learning0
Position: Federated Foundation Language Model Post-Training Should Focus on Open-Source Models0
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models0
Positive and Unlabeled Federated Learning0
Poster: Sponge ML Model Attacks of Mobile Apps0
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing0
PPA: Preference Profiling Attack Against Federated Learning0
PPBFL: A Privacy Protected Blockchain-based Federated Learning Model0
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population0
PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications0
Tackling the Local Bias in Federated Graph Learning0
PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning 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