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 4426–4450 of 6771 papers

Title	Date	Tasks	Status
Social-Aware Clustered Federated Learning with Customized Privacy Preservation	Dec 25, 2022	Federated Learning	—Unverified
When Do Curricula Work in Federated Learning?	Dec 24, 2022	Federated Learning	CodeCode Available
Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environments	Dec 23, 2022	Federated Learning	—Unverified
Graph Federated Learning with Hidden Representation Sharing	Dec 23, 2022	Federated Learning	—Unverified
AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms	Dec 22, 2022	Federated Learning	—Unverified
Federated Learning -- Methods, Applications and beyond	Dec 22, 2022	Federated LearningTransfer Learning	—Unverified
Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks	Dec 21, 2022	Federated Learning	—Unverified
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs	Dec 21, 2022	Federated LearningMulti-Task Learning	—Unverified
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends	Dec 19, 2022	Adversarial AttackBackdoor Attack	—Unverified
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning	Dec 19, 2022	Federated Learning	—Unverified
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification	Dec 18, 2022	Federated LearningPrivacy Preserving	—Unverified
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders	Dec 17, 2022	DecoderFederated Learning	—Unverified
On Noisy Evaluation in Federated Hyperparameter Tuning	Dec 17, 2022	Federated Learning	CodeCode Available
Toward Data Heterogeneity of Federated Learning	Dec 17, 2022	Federated Learning	—Unverified
Federated Learning with Flexible Control	Dec 16, 2022	Federated LearningStochastic Optimization	—Unverified
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks	Dec 16, 2022	Federated LearningFew-Shot Learning	—Unverified
Mobile Augmented Reality with Federated Learning in the Metaverse	Dec 16, 2022	Federated Learningobject-detection	—Unverified
SplitGP: Achieving Both Generalization and Personalization in Federated Learning	Dec 16, 2022	Federated Learning	—Unverified
Robust Learning Protocol for Federated Tumor Segmentation Challenge	Dec 16, 2022	Federated LearningTumor Segmentation	—Unverified
White-box Inference Attacks against Centralized Machine Learning and Federated Learning	Dec 15, 2022	Federated LearningInference Attack	—Unverified
Deep leakage from gradients	Dec 15, 2022	Federated Learning	—Unverified
Bayesian data fusion with shared priors	Dec 14, 2022	Bayesian InferenceFederated Learning	—Unverified
Hierarchical Over-the-Air FedGradNorm	Dec 14, 2022	Federated LearningMulti-Task Learning	—Unverified
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks	Dec 14, 2022	Federated LearningScheduling	—Unverified
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation	Dec 14, 2022	Federated 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