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
Towards Personalized Federated Learning0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation0
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation0
Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach0
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture0
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning0
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture0
Towards Quantum-Enabled 6G Slicing0
Towards Quantum Federated Learning0
Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis0
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients0
Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing0
Towards Robust Federated Learning via Logits Calibration on Non-IID Data0
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach0
Towards Scalable Wireless Federated Learning: Challenges and Solutions0
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning0
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology0
Towards Sparsified Federated Neuroimaging Models via Weight Pruning0
Towards Sybil Resilience in Decentralized Learning0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search0
Towards Trustworthy Federated Learning0
Towards Trustworthy Federated Learning with Untrusted Participants0
Towards Ubiquitous AI in 6G with Federated Learning0
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning0
Towards Verifiable Federated Learning0
Towards Zero-trust Security for the Metaverse0
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning0
Toward Understanding the Influence of Individual Clients in Federated Learning0
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic0
Traceable Black-box Watermarks for Federated Learning0
Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning0
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning0
Trading-off Accuracy and Communication Cost in Federated Learning0
Trading Off Privacy, Utility and Efficiency in Federated Learning0
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning0
An Efficient Federated Learning Framework for Training Semantic Communication System0
Training a Tokenizer for Free with Private Federated Learning0
Training Diffusion Models with Federated Learning0
Training Keyword Spotting Models on Non-IID Data with Federated Learning0
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices0
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning0
Training Machine Learning models at the Edge: A Survey0
Training Mixed-Domain Translation Models via Federated Learning0
Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation0
Training Production Language Models without Memorizing User Data0
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework0
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion0
Tram-FL: Routing-based Model Training for Decentralized Federated Learning0
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification0
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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