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 651–675 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation	Oct 29, 2019	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Learning under Distributed Concept Drift	Jun 1, 2022	ClusteringDrift Detection	CodeCode Available	1
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints	May 13, 2022	BenchmarkingFederated Learning	CodeCode Available	1
Specialized federated learning using a mixture of experts	Oct 5, 2020	Federated LearningMixture-of-Experts	CodeCode Available	1
Personalized Federated Learning with Adaptive Batchnorm for Healthcare	Dec 1, 2021	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data	May 29, 2024	Federated Learning	CodeCode Available	1
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks	Nov 28, 2023	Federated LearningImage Generation	CodeCode Available	1
Federated Learning with Extremely Noisy Clients via Negative Distillation	Dec 20, 2023	Federated LearningKnowledge Distillation	CodeCode Available	1
Federated Learning with Label Distribution Skew via Logits Calibration	Sep 1, 2022	Federated Learning	CodeCode Available	1
Federated Learning with Label-Masking Distillation	Sep 20, 2024	Federated LearningPrivacy Preserving	CodeCode Available	1
Decentralized Federated Learning: A Segmented Gossip Approach	Aug 21, 2019	Federated Learning	CodeCode Available	1
Federated Learning with Partial Model Personalization	Apr 8, 2022	Federated Learningmodel	CodeCode Available	1
Federated Learning with Spiking Neural Networks	Jun 11, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Learning with Taskonomy for Non-IID Data	Mar 29, 2021	FairnessFederated Learning	CodeCode Available	1
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification	Jul 27, 2023	Federated Learningimage-classification	CodeCode Available	1
Federated Multi-Armed Bandits	Jan 28, 2021	Federated LearningMulti-Armed Bandits	CodeCode Available	1
Federated Multi-Task Learning under a Mixture of Distributions	Aug 23, 2021	FairnessFederated Learning	CodeCode Available	1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing	Jul 20, 2023	Federated LearningOne-Shot Learning	CodeCode Available	1
Omnidirectional Transfer for Quasilinear Lifelong Learning	Apr 27, 2020	Federated LearningLifelong learning	CodeCode Available	1
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence	Oct 21, 2019	Federated Learning	CodeCode Available	1
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning	Jun 18, 2021	Adversarial RobustnessFederated Learning	CodeCode Available	1
Federated Self-supervised Learning for Video Understanding	Jul 5, 2022	Action RecognitionFederated Learning	CodeCode Available	1
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning	Jun 22, 2020	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
ARFED: Attack-Resistant Federated averaging based on outlier elimination	Nov 8, 2021	Data PoisoningFederated 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