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 4526–4550 of 6771 papers

Title	Date	Tasks	Status	Hype
Personalized Subgraph Federated Learning	Jun 21, 2022	Federated Learning	CodeCode Available	1
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning	Jun 21, 2022	Federated Learning	—Unverified	0
sqSGD: Locally Private and Communication Efficient Federated Learning	Jun 21, 2022	Federated LearningPrivacy Preserving	—Unverified	0
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates	Jun 21, 2022	Federated Learning	—Unverified	0
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning	Jun 21, 2022	BIG-bench Machine LearningEnsemble Learning	—Unverified	0
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity	Jun 21, 2022	Federated Learning	—Unverified	0
Robust One Round Federated Learning with Predictive Space Bayesian Inference	Jun 20, 2022	Bayesian InferenceFederated Learning	CodeCode Available	0
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression	Jun 20, 2022	Federated LearningPrivacy Preserving	CodeCode Available	0
Shuffle Gaussian Mechanism for Differential Privacy	Jun 20, 2022	Federated Learning	CodeCode Available	1
Communication-Efficient Federated Learning With Data and Client Heterogeneity	Jun 20, 2022	Federated Learning	—Unverified	0
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation	Jun 20, 2022	Data AugmentationDomain Generalization	CodeCode Available	1
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm	Jun 20, 2022	Federated Learning	—Unverified	0
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis	Jun 20, 2022	Federated LearningPrivacy Preserving	CodeCode Available	0
Decoupled Federated Learning for ASR with Non-IID Data	Jun 18, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Secure Embedding Aggregation for Federated Representation Learning	Jun 18, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Pisces: Efficient Federated Learning via Guided Asynchronous Training	Jun 18, 2022	Federated LearningNavigate	CodeCode Available	1
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning	Jun 18, 2022	BenchmarkingFairness	CodeCode Available	0
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning	Jun 17, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare	Jun 17, 2022	Federated LearningKnowledge Distillation	CodeCode Available	2
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification	Jun 17, 2022	Federated LearningFew-Shot Learning	CodeCode Available	1
Federated learning with incremental clustering for heterogeneous data	Jun 17, 2022	ClusteringFederated Learning	—Unverified	0
On Privacy and Personalization in Cross-Silo Federated Learning	Jun 16, 2022	Federated LearningMulti-Task Learning	CodeCode Available	1
Personalized Federated Learning via Variational Bayesian Inference	Jun 16, 2022	Bayesian InferenceDiversity	CodeCode Available	1
Using adversarial images to improve outcomes of federated learning for non-IID data	Jun 16, 2022	Federated Learningimage-classification	—Unverified	0
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	Jun 16, 2022	BIG-bench Machine LearningFederated 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