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 3576–3600 of 6771 papers

Title	Date	Tasks	Status
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients	Nov 12, 2024	Federated Learning	—Unverified
On the Convergence of Federated Averaging with Cyclic Client Participation	Feb 6, 2023	Federated Learning	—Unverified
On the Convergence of FedProx with Extrapolation and Inexact Prox	Oct 2, 2024	Federated Learning	—Unverified
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning	Jan 27, 2022	Federated LearningOpen-Ended Question Answering	—Unverified
On the Convergence of Local Descent Methods in Federated Learning	Oct 31, 2019	Distributed OptimizationFederated Learning	—Unverified
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models	Feb 8, 2024	Federated LearningGPU	—Unverified
On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI	Apr 1, 2021	Federated Learning	—Unverified
On the Decentralization of Blockchain-enabled Asynchronous Federated Learning	May 20, 2022	Edge-computingFederated Learning	—Unverified
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks	Apr 15, 2020	Federated Learning	—Unverified
On the Design of Communication-Efficient Federated Learning for Health Monitoring	Nov 30, 2022	Federated LearningTransfer Learning	—Unverified
On the dynamics of multi agent nonlinear filtering and learning	Sep 7, 2023	Federated Learning	—Unverified
On the Effect of Defections in Federated Learning and How to Prevent Them	Nov 28, 2023	Federated Learning	—Unverified
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning	Dec 2, 2022	Federated LearningMeta-Learning	—Unverified
On the Distributed Evaluation of Generative Models	Oct 18, 2023	AvgFederated Learning	—Unverified
Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy	Feb 19, 2023	Data CompressionFederated Learning	—Unverified
On the Federated Learning Framework for Cooperative Perception	Apr 26, 2024	Autonomous VehiclesDecision Making	—Unverified
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge	Dec 21, 2021	BIG-bench Machine LearningEdge-computing	—Unverified
On the Generalization of Wasserstein Robust Federated Learning	Jun 3, 2022	Domain AdaptationFederated Learning	—Unverified
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space	Feb 23, 2023	Binary ClassificationDomain Adaptation	—Unverified
On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks	Apr 29, 2024	BenchmarkingFederated Learning	—Unverified
On the Impact of Device and Behavioral Heterogeneity in Federated Learning	Feb 15, 2021	FairnessFederated Learning	—Unverified
On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective	Apr 16, 2021	Decision MakingFederated Learning	—Unverified
On the Initial Behavior Monitoring Issues in Federated Learning	Sep 11, 2021	Federated Learningimage-classification	—Unverified
On the Local Cache Update Rules in Streaming Federated Learning	Mar 28, 2023	Federated Learningimage-classification	—Unverified
On the Performance of Gradient Tracking with Local Updates	Oct 10, 2022	Federated Learning	—Unverified

Show:10 25 50

← PrevPage 144 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