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 3501–3525 of 6771 papers

Title	Date	Tasks	Status
Differentially Private Multi-Site Treatment Effect Estimation	Oct 10, 2023	Causal InferenceFederated Learning	—Unverified
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication	Oct 10, 2023	Federated Learning	—Unverified
Secure Decentralized Learning with Blockchain	Oct 10, 2023	Federated Learning	—Unverified
Federated Learning with Reduced Information Leakage and Computation	Oct 10, 2023	Federated LearningPrivacy Preserving	CodeCode Available
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks	Oct 9, 2023	Federated Learning	—Unverified
Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning	Oct 9, 2023	ClusteringFederated Learning	—Unverified
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning	Oct 9, 2023	Federated Learning	—Unverified
Text-driven Prompt Generation for Vision-Language Models in Federated Learning	Oct 9, 2023	Federated Learningimage-classification	—Unverified
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications	Oct 8, 2023	Federated LearningSurvey	—Unverified
Towards Scalable Wireless Federated Learning: Challenges and Solutions	Oct 8, 2023	Federated LearningPrivacy Preserving	—Unverified
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization	Oct 8, 2023	Distributed OptimizationFederated Learning	—Unverified
Asymmetrically Decentralized Federated Learning	Oct 8, 2023	Federated Learning	—Unverified
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles	Oct 7, 2023	Autonomous VehiclesDepth Estimation	—Unverified
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning	Oct 6, 2023	BenchmarkingFederated Learning	—Unverified
The Role of Federated Learning in a Wireless World with Foundation Models	Oct 6, 2023	Federated Learning	—Unverified
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification	Oct 6, 2023	Anomaly DetectionFederated Learning	—Unverified
Utilizing Free Clients in Federated Learning for Focused Model Enhancement	Oct 6, 2023	Federated Learning	—Unverified
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction	Oct 5, 2023	Document AIFederated Learning	—Unverified
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?	Oct 5, 2023	compressed sensingFederated Learning	—Unverified
Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning	Oct 5, 2023	Federated Learningvalid	—Unverified
Solving a Class of Non-Convex Minimax Optimization in Federated Learning	Oct 5, 2023	Federated Learning	CodeCode Available
Recent Methodological Advances in Federated Learning for Healthcare	Oct 4, 2023	Federated Learning	—Unverified
Heterogeneous Federated Learning Using Knowledge Codistillation	Oct 4, 2023	Federated Learningimage-classification	—Unverified
Learning to Prompt Your Domain for Vision-Language Models	Oct 4, 2023	Domain AdaptationFederated Learning	—Unverified
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt	Oct 4, 2023	Contrastive LearningFederated 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