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 47514800 of 6771 papers

TitleStatusHype
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
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting0
Translating Federated Learning Algorithms in Python into CSP Processes Using ChatGPT0
Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection0
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain0
Traversal Learning Coordination For Lossless And Efficient Distributed Learning0
Tree-based Models for Vertical Federated Learning: A Survey0
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency0
Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems0
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning0
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning0
TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance0
Trustformer: A Trusted Federated Transformer0
Trustworthy Federated Learning: A Survey0
Trustworthy Federated Learning: Privacy, Security, and Beyond0
Trustworthy Federated Learning via Blockchain0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune0
Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchain0
Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling0
Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems0
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features0
Tunable Soft Prompts are Messengers in Federated Learning0
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning0
Turning Privacy-preserving Mechanisms against Federated Learning0
Turn Signal Prediction: A Federated Learning Case Study0
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning0
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning0
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks0
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction0
UA-PDFL: A Personalized Approach for Decentralized Federated Learning0
UAV-Aided Multi-Community Federated Learning0
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning0
UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing0
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach0
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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