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 1851–1875 of 6771 papers

Title	Date	Tasks	Status
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints	May 15, 2020	Federated LearningQuantization	—Unverified
Age-of-Gradient Updates for Federated Learning over Random Access Channels	Oct 15, 2024	Federated Learning	—Unverified
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data	Aug 11, 2024	Federated Learning	—Unverified
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup	Jul 30, 2023	Federated Learning	—Unverified
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information	May 9, 2025	Federated Learning	—Unverified
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning	Apr 9, 2022	Federated Learning	—Unverified
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing	May 2, 2023	Edge-computingFederated Learning	—Unverified
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition	May 28, 2022	Federated LearningPrivacy Preserving	—Unverified
Efficient Federated Learning with Timely Update Dissemination	Jul 8, 2025	Federated Learning	—Unverified
Efficient Federated Split Learning for Large Language Models over Communication Networks	Apr 20, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation	Nov 17, 2024	Federated Learning	—Unverified
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection	Oct 23, 2024	Federated Learning	—Unverified
Efficient Fully Distributed Federated Learning with Adaptive Local Links	Mar 23, 2022	Autonomous VehiclesFederated Learning	—Unverified
Efficient Image Representation Learning with Federated Sampled Softmax	Mar 9, 2022	DescriptiveFederated Learning	—Unverified
Efficient Language Model Architectures for Differentially Private Federated Learning	Mar 12, 2024	Computational EfficiencyFederated Learning	—Unverified
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning	Dec 16, 2024	Federated LearningPrivacy Preserving	—Unverified
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization	Mar 22, 2024	Domain GeneralizationFederated Learning	—Unverified
Efficient Model Compression for Hierarchical Federated Learning	May 27, 2024	Edge-computingFederated Learning	—Unverified
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation	Aug 29, 2023	Federated Learning	—Unverified
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning	Mar 14, 2024	Federated LearningPersonalized Federated Learning	—Unverified
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems	Jan 8, 2022	Federated LearningPrivacy Preserving	—Unverified
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments	Nov 15, 2020	Federated LearningModel Poisoning	—Unverified
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain	Jul 8, 2024	Federated LearningIndoor Localization	—Unverified
Efficient Privacy Preserving Edge Computing Framework for Image Classification	May 10, 2020	ClassificationData Compression	—Unverified
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning	May 9, 2024	BenchmarkingFederated Learning	—Unverified

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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