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 44014450 of 6771 papers

TitleStatusHype
SPEFL: Efficient Security and Privacy Enhanced Federated Learning Against Poisoning Attacks0
Bias-Eliminating Augmentation Learning for Debiased Federated Learning0
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal EffectsCode0
Global Balanced Experts for Federated Long-Tailed LearningCode0
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation0
Robust Heterogeneous Federated Learning under Data CorruptionCode0
Generative Gradient Inversion via Over-Parameterized Networks in Federated LearningCode0
Elastic Aggregation for Federated Optimization0
Reliable and Interpretable Personalized Federated Learning0
Fairness and Effectiveness in Federated Learning on Non-independent and Identically Distributed Data0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?0
Mutual Information Regularization for Vertical Federated Learning0
CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation LossCode0
Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity0
Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization0
FedICT: Federated Multi-task Distillation for Multi-access Edge ComputingCode0
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management0
Graph Federated Learning for CIoT Devices in Smart Home ApplicationsCode0
Characterization of the Global Bias Problem in Aerial Federated Learning0
Proof of Swarm Based Ensemble Learning for Federated Learning Applications0
CC-FedAvg: Computationally Customized Federated Averaging0
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions0
A Survey on Federated Recommendation Systems0
Democratising Knowledge Representation with BioCypher0
Social-Aware Clustered Federated Learning with Customized Privacy Preservation0
When Do Curricula Work in Federated Learning?Code0
Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environments0
Graph Federated Learning with Hidden Representation Sharing0
AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms0
Federated Learning -- Methods, Applications and beyond0
Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks0
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends0
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning0
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification0
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders0
On Noisy Evaluation in Federated Hyperparameter TuningCode0
Toward Data Heterogeneity of Federated Learning0
Federated Learning with Flexible Control0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks0
Mobile Augmented Reality with Federated Learning in the Metaverse0
SplitGP: Achieving Both Generalization and Personalization in Federated Learning0
Robust Learning Protocol for Federated Tumor Segmentation Challenge0
White-box Inference Attacks against Centralized Machine Learning and Federated Learning0
Deep leakage from gradients0
Bayesian data fusion with shared priors0
Hierarchical Over-the-Air FedGradNorm0
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks0
FedSkip: Combatting Statistical Heterogeneity with Federated Skip AggregationCode0
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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