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 601–625 of 6771 papers

Title	Date	Tasks	Status	Hype
Relay-Assisted Cooperative Federated Learning	Jul 20, 2021	Federated Learning	CodeCode Available	1
DeceFL: A Principled Decentralized Federated Learning Framework	Jul 15, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Self-Training for Semi-Supervised Audio Recognition	Jul 14, 2021	Audio ClassificationFederated Learning	CodeCode Available	1
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning	Jul 9, 2021	Federated Learning	CodeCode Available	1
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform	Jul 8, 2021	BIG-bench Machine LearningBinary Classification	CodeCode Available	1
RoFL: Robustness of Secure Federated Learning	Jul 7, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease	Jul 7, 2021	Action RecognitionData Augmentation	CodeCode Available	1
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging	Jul 6, 2021	Binary ClassificationFederated Learning	CodeCode Available	1
On Bridging Generic and Personalized Federated Learning for Image Classification	Jul 2, 2021	ClassificationFederated Learning	CodeCode Available	1
Gradient-Leakage Resilient Federated Learning	Jul 2, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
FedCMR: Federated Cross-Modal Retrieval	Jul 1, 2021	Cross-Modal RetrievalFederated Learning	CodeCode Available	1
FedMix: Approximation of Mixup under Mean Augmented Federated Learning	Jul 1, 2021	Data AugmentationFederated Learning	CodeCode Available	1
Personalized Federated Learning with Gaussian Processes	Jun 29, 2021	Federated LearningGaussian Processes	CodeCode Available	1
Benchmarking Differential Privacy and Federated Learning for BERT Models	Jun 26, 2021	BenchmarkingFederated Learning	CodeCode Available	1
Federated Graph Classification over Non-IID Graphs	Jun 25, 2021	ClassificationClustering	CodeCode Available	1
Subgraph Federated Learning with Missing Neighbor Generation	Jun 25, 2021	Federated LearningGraph Mining	CodeCode Available	1
An Efficient Approach for Cross-Silo Federated Learning to Rank	Jun 21, 2021	Federated LearningLearning-To-Rank	CodeCode Available	1
Federated Learning with Positive and Unlabeled Data	Jun 21, 2021	Federated Learning	CodeCode Available	1
FedCM: Federated Learning with Client-level Momentum	Jun 21, 2021	Federated Learning	CodeCode Available	1
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective	Jun 19, 2021	Federated LearningInference Attack	CodeCode Available	1
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning	Jun 18, 2021	Adversarial RobustnessFederated Learning	CodeCode Available	1
Accumulative Poisoning Attacks on Real-time Data	Jun 18, 2021	Federated Learning	CodeCode Available	1
Optimality and Stability in Federated Learning: A Game-theoretic Approach	Jun 17, 2021	Federated Learning	CodeCode Available	1
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching	Jun 16, 2021	Federated Learningimage-classification	CodeCode Available	1
Optimal Accounting of Differential Privacy via Characteristic Function	Jun 16, 2021	Federated 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