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 426–450 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation	Apr 30, 2022	Federated LearningLong-tail Learning	CodeCode Available	1	5
Differentially Private Learning with Adaptive Clipping	May 9, 2019	Federated Learning	CodeCode Available	1	5
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design	Jul 28, 2023	Experimental DesignFederated Learning	CodeCode Available	1	5
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation	Mar 13, 2023	Continual LearningExemplar-Free	CodeCode Available	1	5
Differentially Private Vertical Federated Clustering	Aug 2, 2022	ClusteringFederated Learning	CodeCode Available	1	5
DistFL: Distribution-aware Federated Learning for Mobile Scenarios	Oct 22, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles	Mar 5, 2021	Federated LearningKnowledge Distillation	CodeCode Available	1	5
Dopamine: Differentially Private Federated Learning on Medical Data	Jan 27, 2021	Federated Learning	CodeCode Available	1	5
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform	Jul 8, 2021	BIG-bench Machine LearningBinary Classification	CodeCode Available	1	5
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation	Apr 14, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1	5
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning	Mar 11, 2023	Federated Learning	CodeCode Available	1	5
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation	Aug 15, 2022	Federated Learning	CodeCode Available	1	5
BAFFLE: A Baseline of Backpropagation-Free Federated Learning	Jan 28, 2023	Federated LearningQuantization	CodeCode Available	1	5
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification	Oct 8, 2024	Domain AdaptationFederated Learning	CodeCode Available	1	5
An Efficient Framework for Clustered Federated Learning	Jun 7, 2020	ClusteringFederated Learning	CodeCode Available	1	5
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning	Apr 7, 2020	Federated Learning	CodeCode Available	1	5
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning	Feb 8, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1	5
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging	Jul 11, 2024	DiversityFederated Learning	CodeCode Available	1	5
Federated Learning Meets Multi-objective Optimization	Jun 20, 2020	FairnessFederated Learning	CodeCode Available	1	5
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service	Aug 17, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Exploiting Shared Representations for Personalized Federated Learning	Feb 14, 2021	Federated LearningMeta-Learning	CodeCode Available	1	5
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation	Jan 28, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1	5
Efficient Personalized Federated Learning via Sparse Model-Adaptation	May 4, 2023	Federated Learningmodel	CodeCode Available	1	5
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance	Jun 27, 2022	Federated Learning	CodeCode Available	1	5
Applied Federated Learning: Improving Google Keyboard Query Suggestions	Dec 7, 2018	BIG-bench Machine LearningFederated Learning	CodeCode Available	1	5

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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