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 39013950 of 6771 papers

TitleStatusHype
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations0
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging0
Unlocking the Potential of Federated Learning for Deeper Models0
Federated Deep Learning for Intrusion Detection in IoT Networks0
When Decentralized Optimization Meets Federated Learning0
Over-the-Air Federated Learning in Satellite systems0
A Privacy-Preserving Federated Learning Approach for Kernel methods0
Jammer classification with Federated Learning0
Confidence-based federated distillation for vision-based lane-centering0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation0
Improving Accelerated Federated Learning with Compression and Importance Sampling0
Cross-Modal Vertical Federated Learning for MRI Reconstruction0
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation0
Resilient Constrained Learning0
A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions0
Over-the-Air Federated Learning In Broadband Communication0
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation0
Federated Domain Generalization: A Survey0
Decentralized Federated Learning: A Survey and Perspective0
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations0
Federated Learning of Models Pre-Trained on Different Features with Consensus GraphsCode0
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions0
Beta Thalassemia Carriers detection empowered federated Learning0
Covert Communication Based on the Poisoning Attack in Federated Learning0
FACT: Federated Adversarial Cross TrainingCode0
RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning0
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging0
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications0
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning0
Byzantine-Robust Clustered Federated LearningCode0
CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning0
Federated Learning in the Presence of Adversarial Client Unavailability0
FedCSD: A Federated Learning Based Approach for Code-Smell Detection0
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning0
An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity0
A Federated Channel Modeling System using Generative Neural Networks0
Reducing Communication for Split Learning by Randomized Top-k Sparsification0
Global Layers: Non-IID Tabular Federated LearningCode0
Deep Equilibrium Models Meet Federated Learning0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity0
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning0
Federated Empirical Risk Minimization via Second-Order Method0
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning0
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New AlgorithmsCode0
A Framework for Incentivized Collaborative Learning0
Parameter-Efficient Fine-Tuning without Introducing New LatencyCode0
Secure Vertical Federated Learning Under Unreliable Connectivity0
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices0
FAVANO: Federated AVeraging with Asynchronous NOdes0
Distributed Trust Through the Lens of Software Architecture0
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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