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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 4051–4100 of 6771 papers

Title	Date	Tasks	Status
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis	Apr 26, 2023	ClusteringFault Diagnosis	CodeCode Available
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models	Apr 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Bayesian Federated Learning: A Survey	Apr 26, 2023	Federated LearningPrivacy Preserving	—Unverified
User-Centric Federated Learning: Trading off Wireless Resources for Personalization	Apr 25, 2023	Federated LearningPrivacy Preserving	—Unverified
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM	Apr 25, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things	Apr 25, 2023	Federated Learning	—Unverified
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning	Apr 24, 2023	Federated LearningTumor Segmentation	—Unverified
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation	Apr 24, 2023	Domain AdaptationFederated Learning	—Unverified
More Communication Does Not Result in Smaller Generalization Error in Federated Learning	Apr 24, 2023	Federated Learning	—Unverified
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization	Apr 23, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration	Apr 22, 2023	Binary ClassificationFederated Learning	—Unverified
Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack	Apr 22, 2023	Federated Learning	CodeCode Available
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications	Apr 21, 2023	Federated Learning	—Unverified
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning	Apr 21, 2023	Federated LearningModel Poisoning	—Unverified
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning	Apr 21, 2023	Federated Learning	—Unverified
Federated Compositional Deep AUC Maximization	Apr 20, 2023	Federated Learning	—Unverified
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning	Apr 20, 2023	Federated Learningimage-classification	—Unverified
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing	Apr 19, 2023	Federated LearningNetwork Pruning	—Unverified
Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks	Apr 18, 2023	Federated Learning	—Unverified
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging	Apr 18, 2023	Federated LearningSegmentation	—Unverified
BadVFL: Backdoor Attacks in Vertical Federated Learning	Apr 18, 2023	Backdoor AttackFederated Learning	—Unverified
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives	Apr 17, 2023	Federated LearningLoad Forecasting	—Unverified
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation	Apr 17, 2023	Federated Learning	—Unverified
FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation	Apr 17, 2023	Federated Learning	—Unverified
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models	Apr 17, 2023	Federated LearningImputation	—Unverified
SplitAMC: Split Learning for Robust Automatic Modulation Classification	Apr 17, 2023	ClassificationFederated Learning	—Unverified
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification	Apr 16, 2023	Federated Learningimage-classification	—Unverified
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain	Apr 16, 2023	Computational EfficiencyFederated Learning	—Unverified
Practical Differentially Private and Byzantine-resilient Federated Learning	Apr 15, 2023	Federated LearningPrivacy Preserving	CodeCode Available
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training	Apr 15, 2023	Federated Learning	—Unverified
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates	Apr 15, 2023	Federated Learning	—Unverified
IP-FL: Incentivized and Personalized Federated Learning	Apr 15, 2023	ClusteringFederated Learning	—Unverified
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy	Apr 15, 2023	Federated Learning	—Unverified
Data, Competition, and Digital Platforms	Apr 15, 2023	Federated Learning	—Unverified
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data	Apr 15, 2023	Federated LearningImage Reconstruction	CodeCode Available
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review	Apr 14, 2023	ArticlesDistributed Computing	—Unverified
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training	Apr 14, 2023	Federated Learning	—Unverified
Scale Federated Learning for Label Set Mismatch in Medical Image Classification	Apr 14, 2023	Federated Learningimage-classification	CodeCode Available
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition	Apr 14, 2023	Action RecognitionContinual Learning	—Unverified
Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks	Apr 14, 2023	FairnessFederated Learning	—Unverified
Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks	Apr 13, 2023	Federated Learning	—Unverified
Edge-cloud Collaborative Learning with Federated and Centralized Features	Apr 12, 2023	Edge-computingFederated Learning	—Unverified
HPN: Personalized Federated Hyperparameter Optimization	Apr 11, 2023	Federated LearningHyperparameter Optimization	—Unverified
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense	Apr 11, 2023	Adversarial AttackAttribute	—Unverified
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery	Apr 11, 2023	Drug DiscoveryFederated Learning	—Unverified
A Game-theoretic Framework for Privacy-preserving Federated Learning	Apr 11, 2023	Federated LearningPrivacy Preserving	—Unverified
Balancing Privacy and Performance for Private Federated Learning Algorithms	Apr 11, 2023	Federated Learning	—Unverified
TinyReptile: TinyML with Federated Meta-Learning	Apr 11, 2023	Federated LearningMeta-Learning	—Unverified
Federated Learning with Classifier Shift for Class Imbalance	Apr 11, 2023	Federated Learning	—Unverified
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning	Apr 11, 2023	Federated Learning	—Unverified

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All datasets Cityscapes heterogeneous Landmarks-User-160k CIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SiloBN + ASAM	mIoU	49.75	—	Unverified
2	SiloBN + SAM	mIoU	49.1	—	Unverified
3	SiloBN	mIoU	45.96	—	Unverified
4	FedSAM + SWA	mIoU	43.42	—	Unverified
5	FedASAM + SWA	mIoU	43.02	—	Unverified
6	FedAvg + SWA	mIoU	42.48	—	Unverified
7	FedASAM	mIoU	42.27	—	Unverified
8	FedSAM	mIoU	41.22	—	Unverified
9	FedAvg	mIoU	38.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	Acc@1-1262Clients	68.32	—	Unverified
2	FedSAM + SWA	Acc@1-1262Clients	68.12	—	Unverified
3	FedAvg + SWA	Acc@1-1262Clients	67.52	—	Unverified
4	FedASAM	Acc@1-1262Clients	64.23	—	Unverified
5	FedSAM	Acc@1-1262Clients	63.72	—	Unverified
6	FedAvg	Acc@1-1262Clients	61.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.64	—	Unverified
2	FedASAM	ACC@1-100Clients	39.76	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.51	—	Unverified
4	FedSAM	ACC@1-100Clients	36.93	—	Unverified
5	FedAvg	ACC@1-100Clients	36.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	41.62	—	Unverified
2	FedASAM	ACC@1-100Clients	40.81	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.24	—	Unverified
4	FedAvg	ACC@1-100Clients	38.59	—	Unverified
5	FedSAM	ACC@1-100Clients	38.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.72	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	46.76	—	Unverified
3	FedASAM	ACC@1-100Clients	46.58	—	Unverified
4	FedSAM	ACC@1-100Clients	44.84	—	Unverified
5	FedAvg	ACC@1-100Clients	41.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.27	—	Unverified
2	FedASAM	ACC@1-100Clients	47.78	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	46.47	—	Unverified
4	FedSAM	ACC@1-100Clients	46.05	—	Unverified
5	FedAvg	ACC@1-100Clients	42.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	49.17	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	47.96	—	Unverified
3	FedASAM	ACC@1-100Clients	45.61	—	Unverified
4	FedSAM	ACC@1-100Clients	44.73	—	Unverified
5	FedAvg	ACC@1-100Clients	40.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.01	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	39.3	—	Unverified
3	FedASAM	ACC@1-100Clients	36.04	—	Unverified
4	FedSAM	ACC@1-100Clients	31.04	—	Unverified
5	FedAvg	ACC@1-100Clients	30.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.97	—	Unverified
2	FedASAM + SWA	ACC@1-100Clients	54.79	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.67	—	Unverified
4	FedSAM	ACC@1-100Clients	53.39	—	Unverified
5	FedAvg	ACC@1-100Clients	50.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.5	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	54.36	—	Unverified
3	FedASAM + SWA	ACC@1-100Clients	54.1	—	Unverified
4	FedSAM	ACC@1-100Clients	53.97	—	Unverified
5	FedAvg	ACC@1-100Clients	50.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.81	—	Unverified
2	FedSAM	ACC@1-100Clients	54.01	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.9	—	Unverified
4	FedASAM + SWA	ACC@1-100Clients	53.86	—	Unverified
5	FedAvg	ACC@1-100Clients	49.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaBest	Average Top-1 Accuracy	56.2	—	Unverified