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 326–350 of 6771 papers

Title	Date	Tasks	Status	Hype
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation	Mar 13, 2023	Continual LearningExemplar-Free	CodeCode Available	1
Multi-metrics adaptively identifies backdoors in Federated learning	Mar 12, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning	Mar 11, 2023	Federated Learning	CodeCode Available	1
Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks	Mar 9, 2023	DiversityFederated Learning	CodeCode Available	1
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning	Mar 1, 2023	Federated Learning	CodeCode Available	1
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting	Feb 28, 2023	Continual LearningFederated Learning	CodeCode Available	1
Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing	Feb 26, 2023	Autonomous VehiclesEdge-computing	CodeCode Available	1
Active Membership Inference Attack under Local Differential Privacy in Federated Learning	Feb 24, 2023	Federated LearningInference Attack	CodeCode Available	1
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy	Feb 21, 2023	Federated Learning	CodeCode Available	1
Federated Learning for ASR based on Wav2vec 2.0	Feb 20, 2023	Federated LearningLanguage Modeling	CodeCode Available	1
Multimodal Federated Learning via Contrastive Representation Ensemble	Feb 17, 2023	Federated LearningImage-text Retrieval	CodeCode Available	1
Revisiting Weighted Aggregation in Federated Learning with Neural Networks	Feb 14, 2023	Federated Learning	CodeCode Available	1
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees	Feb 13, 2023	Federated LearningGeneralization Bounds	CodeCode Available	1
One-Shot Federated Conformal Prediction	Feb 13, 2023	Conformal PredictionFederated Learning	CodeCode Available	1
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting	Feb 13, 2023	Federated Learning	CodeCode Available	1
XFL: A High Performace, Lightweighted Federated Learning Framework	Feb 10, 2023	Federated LearningVocal Bursts Intensity Prediction	CodeCode Available	1
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach	Feb 8, 2023	Distributed OptimizationFederated Learning	CodeCode Available	1
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection	Feb 7, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
No One Left Behind: Real-World Federated Class-Incremental Learning	Feb 2, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection	Feb 1, 2023	Autonomous DrivingFederated Learning	CodeCode Available	1
FedFA: Federated Feature Augmentation	Jan 30, 2023	Federated Learning	CodeCode Available	1
BAFFLE: A Baseline of Backpropagation-Free Federated Learning	Jan 28, 2023	Federated LearningQuantization	CodeCode Available	1
Federated Recommendation with Additive Personalization	Jan 22, 2023	Federated LearningRecommendation Systems	CodeCode Available	1
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data	Jan 22, 2023	Federated LearningPrompt Learning	CodeCode Available	1
On the Vulnerability of Backdoor Defenses for Federated Learning	Jan 19, 2023	Backdoor AttackFederated Learning	CodeCode Available	1

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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