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 4951–4975 of 6771 papers

Title	Date	Tasks	Status	Hype
Architecture Agnostic Federated Learning for Neural Networks	Feb 15, 2022	Federated Learning	—Unverified	0
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT	Feb 15, 2022	Deep Reinforcement LearningEdge-computing	CodeCode Available	1
Federated Graph Neural Networks: Overview, Techniques and Challenges	Feb 15, 2022	Federated LearningSurvey	—Unverified	0
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification	Feb 15, 2022	Federated LearningInference Attack	CodeCode Available	1
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy	Feb 15, 2022	Federated Learning	—Unverified	0
UA-FedRec: Untargeted Attack on Federated News Recommendation	Feb 14, 2022	Federated LearningNews Recommendation	CodeCode Available	1
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning	Feb 14, 2022	Contrastive LearningFederated Learning	—Unverified	0
FLHub: a Federated Learning model sharing service	Feb 14, 2022	BIG-bench Machine LearningDistributed Computing	—Unverified	0
Do Gradient Inversion Attacks Make Federated Learning Unsafe?	Feb 14, 2022	Federated LearningPrivacy Preserving	—Unverified	0
On the Convergence of Clustered Federated Learning	Feb 13, 2022	Federated Learning	CodeCode Available	0
On Federated Learning with Energy Harvesting Clients	Feb 12, 2022	Federated LearningScheduling	—Unverified	0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning	Feb 12, 2022	Distributed OptimizationFederated Learning	—Unverified	0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing	Feb 11, 2022	Distributed OptimizationFederated Learning	—Unverified	0
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction	Feb 10, 2022	Federated LearningVertical Federated Learning	—Unverified	0
PPA: Preference Profiling Attack Against Federated Learning	Feb 10, 2022	Federated LearningInference Attack	—Unverified	0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning	Feb 10, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	0
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling	Feb 10, 2022	Federated LearningRecommendation Systems	—Unverified	0
FedQAS: Privacy-aware machine reading comprehension with federated learning	Feb 9, 2022	Conversational Question AnsweringFederated Learning	CodeCode Available	0
Vertical Federated Learning: Challenges, Methodologies and Experiments	Feb 9, 2022	Federated LearningVertical Federated Learning	—Unverified	0
Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning	Feb 9, 2022	DiversityEdge-computing	—Unverified	0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection	Feb 9, 2022	Anomaly DetectionFederated Learning	—Unverified	0
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees	Feb 8, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Federated Learning of Generative Image Priors for MRI Reconstruction	Feb 8, 2022	Federated LearningMRI Reconstruction	CodeCode Available	1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning	Feb 8, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
Learnings from Federated Learning in the Real world	Feb 8, 2022	Federated LearningNatural Language Understanding	—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