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 4426–4450 of 6771 papers

Title	Date	Tasks	Status	Hype
Slimmable Quantum Federated Learning	Jul 20, 2022	Federated Learning	—Unverified	0
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design	Jul 19, 2022	Federated LearningQuantization	—Unverified	0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios	Jul 19, 2022	Federated LearningQ-Learning	—Unverified	0
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing	Jul 19, 2022	Federated Learning	—Unverified	0
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients	Jul 19, 2022	Federated LearningModel Poisoning	CodeCode Available	1
Over-the-Air Federated Edge Learning with Hierarchical Clustering	Jul 19, 2022	ClusteringFederated Learning	—Unverified	0
SphereFed: Hyperspherical Federated Learning	Jul 19, 2022	Federated Learning	—Unverified	0
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation	Jul 19, 2022	Contrastive LearningFederated Learning	CodeCode Available	1
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond	Jul 19, 2022	Federated LearningPhilosophy	—Unverified	0
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices	Jul 18, 2022	Federated LearningLanguage Modeling	—Unverified	0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications	Jul 18, 2022	Activity RecognitionAnomaly Detection	—Unverified	0
FLAIR: Federated Learning Annotated Image Repository	Jul 18, 2022	Federated LearningMulti-Label Classification	CodeCode Available	1
Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update	Jul 18, 2022	Federated Learning	—Unverified	0
Study of the performance and scalability of federated learning for medical imaging with intermittent clients	Jul 18, 2022	Federated LearningMedical Image Analysis	—Unverified	0
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR	Jul 17, 2022	Activity RecognitionFederated Learning	—Unverified	0
Federated Continual Learning through distillation in pervasive computing	Jul 17, 2022	Activity RecognitionContinual Learning	—Unverified	0
Fast Composite Optimization and Statistical Recovery in Federated Learning	Jul 17, 2022	Federated Learning	—Unverified	0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain	Jul 17, 2022	Activity RecognitionFederated Learning	—Unverified	0
Multi-Task and Transfer Learning for Federated Learning Applications	Jul 17, 2022	Federated LearningMeta-Learning	—Unverified	0
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning	Jul 17, 2022	Federated LearningRobust Design	—Unverified	0
Collaborative Learning in Kernel-based Bandits for Distributed Users	Jul 16, 2022	Federated Learning	—Unverified	0
Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees	Jul 16, 2022	Federated Learningspeech-recognition	—Unverified	0
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging	Jul 15, 2022	Federated LearningOutlier Detection	CodeCode Available	0
Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations	Jul 15, 2022	Federated Learning	—Unverified	0
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data	Jul 15, 2022	Federated LearningModel Compression	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