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 39514000 of 6771 papers

TitleStatusHype
Privacy Preserving QoE Modeling using Collaborative Learning0
Privacy-preserving quantum federated learning via gradient hiding0
Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data0
Privacy-Preserving Sequential Recommendation with Collaborative Confusion0
Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach0
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning0
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems0
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy0
Privacy Preserving Vertical Federated Learning for Tree-based Models0
Privacy Protection in Prosumer Energy Management Based on Federated Learning0
Privacy Threats Against Federated Matrix Factorization0
Privacy Threats Analysis to Secure Federated Learning0
Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review0
Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning0
Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion0
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches0
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems0
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions0
Private data sharing between decentralized users through the privGAN architecture0
D2P-Fed: Differentially Private Federated Learning With Efficient Communication0
Private Federated Learning In Real World Application -- A Case Study0
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption0
Private Federated Learning with Autotuned Compression0
Private Federated Learning with Domain Adaptation0
Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation0
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses0
Private Language Model Adaptation for Speech Recognition0
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning0
Private Model Personalization Revisited0
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams0
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation0
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges0
Private Wireless Federated Learning with Anonymous Over-the-Air Computation0
PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation0
Privatized Graph Federated Learning0
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning0
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems0
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation0
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information0
Probabilistic Federated Neural Matching0
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data0
Probabilistic Inference for Learning from Untrusted Sources0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification0
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes0
ProFed: a Benchmark for Proximity-based non-IID Federated Learning0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning0
PROFL: A Privacy-Preserving Federated Learning Method with Stringent Defense Against Poisoning Attacks0
ProFL: Performative Robust Optimal Federated Learning0
Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data 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