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 376–400 of 6771 papers

Title	Date	Tasks	Status	Hype
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization	Sep 13, 2023	Federated Learning	CodeCode Available	1
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning	Sep 4, 2020	Bayesian InferenceFederated Learning	CodeCode Available	1
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving	Feb 28, 2022	Autonomous DrivingDomain Generalization	CodeCode Available	1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs	Aug 29, 2022	Federated LearningGraph Learning	CodeCode Available	1
Federated Adaptation for Foundation Model-based Recommendations	May 8, 2024	Federated Learningmodel	CodeCode Available	1
Federated Bayesian Optimization via Thompson Sampling	Oct 20, 2020	Bayesian OptimizationComputational Efficiency	CodeCode Available	1
Federated Class-Incremental Learning	Mar 22, 2022	class-incremental learningClass Incremental Learning	CodeCode Available	1
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks	Dec 7, 2021	Autonomous VehiclesDeep Reinforcement Learning	CodeCode Available	1
FedMood: Federated Learning on Mobile Health Data for Mood Detection	Feb 6, 2021	BIG-bench Machine LearningDepression Detection	CodeCode Available	1
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better	Dec 18, 2021	Federated Learning	CodeCode Available	1
Federated Ensemble-Directed Offline Reinforcement Learning	May 4, 2023	continuous-controlContinuous Control	CodeCode Available	1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning	Dec 16, 2020	Federated LearningIntrusion Detection	CodeCode Available	1
Federated Foundation Models on Heterogeneous Time Series	Dec 12, 2024	Anomaly DetectionFederated Learning	CodeCode Available	1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning	Apr 25, 2023	Contrastive LearningFederated Learning	CodeCode Available	1
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent	Sep 11, 2020	Bayesian InferenceFederated Learning	CodeCode Available	1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations	Aug 21, 2023	Federated Learning	CodeCode Available	1
Federated Knowledge Distillation	Nov 4, 2020	Federated LearningKnowledge Distillation	CodeCode Available	1
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning	Jun 19, 2020	Federated LearningStochastic Optimization	CodeCode Available	1
Federated Learning Based on Dynamic Regularization	Nov 8, 2021	Federated Learning	CodeCode Available	1
Federated Learning for 5G Base Station Traffic Forecasting	Nov 28, 2022	Federated LearningManagement	CodeCode Available	1
Federated Learning for ASR based on Wav2vec 2.0	Feb 20, 2023	Federated LearningLanguage Modeling	CodeCode Available	1
A Dynamic Weighted Federated Learning for Android Malware Classification	Nov 23, 2022	ClassificationFederated Learning	CodeCode Available	1
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing	Oct 14, 2021	Cloud ComputingFederated Learning	CodeCode Available	1
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation	Aug 15, 2022	Federated Learning	CodeCode Available	1
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 16 of 271Next →

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