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 5776–5800 of 6771 papers

Title	Date	Tasks	Status
Cluster-driven Graph Federated Learning over Multiple Domains	Apr 29, 2021	ClusteringFederated Learning	—Unverified
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach	Apr 27, 2021	Anomaly DetectionFederated Learning	—Unverified
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning	Apr 27, 2021	Federated LearningPrivacy Preserving	—Unverified
Towards Fair Federated Learning with Zero-Shot Data Augmentation	Apr 27, 2021	Data AugmentationFairness	—Unverified
Secure and Efficient Federated Learning Through Layering and Sharding Blockchain	Apr 27, 2021	Edge-computingFederated Learning	—Unverified
Simultaneous Wireless Information and Power Transfer for Federated Learning	Apr 26, 2021	Federated Learning	—Unverified
Continual Distributed Learning for Crisis Management	Apr 26, 2021	Continual LearningDisaster Response	—Unverified
A Graph Federated Architecture with Privacy Preserving Learning	Apr 26, 2021	Federated LearningPrivacy Preserving	—Unverified
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia	Apr 26, 2021	Federated LearningGenerative Adversarial Network	—Unverified
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression	Apr 26, 2021	Federated LearningLow-rank compression	—Unverified
Communication-Efficient and Personalized Federated Lottery Ticket Learning	Apr 26, 2021	Federated LearningMulti-Task Learning	—Unverified
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data	Apr 26, 2021	Federated Learning	—Unverified
FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework	Apr 25, 2021	Federated LearningModel Optimization	—Unverified
Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends	Apr 24, 2021	Federated Learning	—Unverified
Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm	Apr 24, 2021	Federated Learning	—Unverified
Robust Federated Learning by Mixture of Experts	Apr 23, 2021	Federated LearningMixture-of-Experts	CodeCode Available
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans	Apr 22, 2021	Federated Learning	—Unverified
Gradient Masked Federated Optimization	Apr 21, 2021	Federated Learning	—Unverified
Turning Federated Learning Systems Into Covert Channels	Apr 21, 2021	Federated LearningModel Poisoning	—Unverified
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things	Apr 21, 2021	Edge-computingFederated Learning	—Unverified
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation	Apr 20, 2021	Federated LearningImage Segmentation	—Unverified
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning	Apr 19, 2021	Federated LearningRepresentation Learning	—Unverified
Research on Resource Allocation for Efficient Federated Learning	Apr 19, 2021	Edge-computingFederated Learning	—Unverified
Federated Learning of User Verification Models Without Sharing Embeddings	Apr 18, 2021	Federated Learning	—Unverified
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks	Apr 18, 2021	BenchmarkingFederated Learning	CodeCode Available

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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