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 801–825 of 6771 papers

Title	Date	Tasks	Status	Hype
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	Oct 14, 2019	Distributed OptimizationFederated Learning	CodeCode Available	1
FedMD: Heterogenous Federated Learning via Model Distillation	Oct 8, 2019	Federated LearningKnowledge Distillation	CodeCode Available	1
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification	Sep 13, 2019	Federated LearningGeneral Classification	CodeCode Available	1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction	Sep 12, 2019	Distributed OptimizationFederated Learning	CodeCode Available	1
Decentralized Federated Learning: A Segmented Gossip Approach	Aug 21, 2019	Federated Learning	CodeCode Available	1
On Analog Gradient Descent Learning over Multiple Access Fading Channels	Aug 20, 2019	Federated Learning	CodeCode Available	1
On the Convergence of FedAvg on Non-IID Data	Jul 4, 2019	Edge-computingFederated Learning	CodeCode Available	1
Interpret Federated Learning with Shapley Values	May 11, 2019	Feature ImportanceFederated Learning	CodeCode Available	1
Differentially Private Learning with Adaptive Clipping	May 9, 2019	Federated Learning	CodeCode Available	1
Towards Federated Learning at Scale: System Design	Feb 4, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Agnostic Federated Learning	Feb 1, 2019	Cloud ComputingDomain Adaptation	CodeCode Available	1
Federated Optimization in Heterogeneous Networks	Dec 14, 2018	Distributed OptimizationFederated Learning	CodeCode Available	1
Applied Federated Learning: Improving Google Keyboard Query Suggestions	Dec 7, 2018	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning	Dec 3, 2018	Deep LearningFederated Learning	CodeCode Available	1
LEAF: A Benchmark for Federated Settings	Dec 3, 2018	Autonomous VehiclesBenchmarking	CodeCode Available	1
Analyzing Federated Learning through an Adversarial Lens	Nov 29, 2018	Federated LearningModel Poisoning	CodeCode Available	1
How To Backdoor Federated Learning	Jul 2, 2018	Anomaly DetectionData Poisoning	CodeCode Available	1
Differentially Private Federated Learning: A Client Level Perspective	Dec 20, 2017	Federated LearningPrivacy Preserving	CodeCode Available	1
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent	May 16, 2017	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Communication-Efficient Learning of Deep Networks from Decentralized Data	Feb 17, 2016	Federated LearningSpeech Recognition	CodeCode Available	1
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints	Jul 17, 2025	Federated LearningImage Generation	CodeCode Available	0
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient	Jul 17, 2025	FairnessFederated Learning	—Unverified	0
Federated Learning for Commercial Image Sources	Jul 17, 2025	Federated Learningimage-classification	—Unverified	0
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks	Jul 16, 2025	Data PoisoningFederated Learning	—Unverified	0
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning	Jul 16, 2025	Data PoisoningFederated 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