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 3601–3625 of 6771 papers

Title	Date	Tasks	Status	Hype
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems	Apr 4, 2023	Autonomous DrivingFederated Learning	CodeCode Available	1
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach	Apr 3, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Federated Kalman Filter for Secure IoT-based Device Monitoring Services	Apr 3, 2023	Federated Learning	—Unverified	0
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity	Apr 3, 2023	Federated Learningmodel	—Unverified	0
Personalized Federated Learning with Local Attention	Apr 2, 2023	Federated Learningimage-classification	—Unverified	0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence	Apr 2, 2023	Distributed OptimizationFederated Learning	—Unverified	0
A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions	Apr 2, 2023	Drug DiscoveryEdge-computing	—Unverified	0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising	Apr 2, 2023	DenoisingFederated Learning	CodeCode Available	0
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence	Apr 1, 2023	Contrastive LearningFederated Learning	—Unverified	0
Benchmarking FedAvg and FedCurv for Image Classification Tasks	Mar 31, 2023	BenchmarkingClassification	—Unverified	0
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis	Mar 31, 2023	Federated Learning	—Unverified	0
Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarios	Mar 30, 2023	Federated LearningPrediction	CodeCode Available	0
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation	Mar 30, 2023	Federated Learning	—Unverified	0
DPP-based Client Selection for Federated Learning with Non-IID Data	Mar 30, 2023	Federated Learning	—Unverified	0
FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA	Mar 29, 2023	ClusteringFederated Learning	—Unverified	0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection	Mar 29, 2023	Anomaly DetectionFederated Learning	—Unverified	0
Federated Learning in MIMO Satellite Broadcast System	Mar 29, 2023	Federated Learning	—Unverified	0
Fair Federated Medical Image Segmentation via Client Contribution Estimation	Mar 29, 2023	FairnessFederated Learning	—Unverified	0
Federated Learning for Heterogeneous Bandits with Unobserved Contexts	Mar 29, 2023	Federated LearningMulti-Armed Bandits	—Unverified	0
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination	Mar 28, 2023	Federated LearningIntrusion Detection	—Unverified	0
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties	Mar 28, 2023	Federated LearningVertical Federated Learning	—Unverified	0
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples	Mar 28, 2023	Federated LearningVertical Federated Learning	—Unverified	0
On the Local Cache Update Rules in Streaming Federated Learning	Mar 28, 2023	Federated Learningimage-classification	—Unverified	0
A Comparative Study of Federated Learning Models for COVID-19 Detection	Mar 28, 2023	Federated LearningMedical Image Analysis	—Unverified	0
FedAgg: Adaptive Federated Learning with Aggregated Gradients	Mar 28, 2023	Federated Learning	—Unverified	0

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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