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 3526–3550 of 6771 papers

Title	Date	Tasks	Status
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework	Oct 4, 2023	Federated Learning	—Unverified
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent	Oct 4, 2023	Federated LearningHyperparameter Optimization	—Unverified
Digital Ethics in Federated Learning	Oct 4, 2023	EthicsFairness	—Unverified
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning	Oct 4, 2023	DiversityFederated Learning	—Unverified
Federated Conditional Stochastic Optimization	Oct 4, 2023	Distributed OptimizationFederated Learning	—Unverified
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning	Oct 4, 2023	Federated Learning	—Unverified
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly	Oct 4, 2023	Computational EfficiencyEdge-computing	—Unverified
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance	Oct 4, 2023	Federated Learning	—Unverified
Federated Wasserstein Distance	Oct 3, 2023	Federated Learning	—Unverified
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks	Oct 3, 2023	Federated Learning	—Unverified
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models	Oct 2, 2023	Federated LearningPrivacy Preserving	—Unverified
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things	Oct 2, 2023	Adversarial AttackFederated Learning	—Unverified
Federated K-means Clustering	Oct 2, 2023	ClusteringFederated Learning	—Unverified
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning	Oct 2, 2023	Federated Learning	—Unverified
Towards Understanding Adversarial Transferability in Federated Learning	Oct 1, 2023	AttributeFederated Learning	—Unverified
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models	Oct 1, 2023	DiagnosticDiversity	CodeCode Available
Intelligent Client Selection for Federated Learning using Cellular Automata	Oct 1, 2023	Federated Learning	CodeCode Available
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning	Sep 30, 2023	Federated LearningModel Compression	—Unverified
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning	Sep 30, 2023	Federated Learning	—Unverified
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation	Sep 30, 2023	Federated Learning	CodeCode Available
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration	Sep 29, 2023	Continual LearningFederated Learning	CodeCode Available
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation	Sep 29, 2023	BenchmarkingFederated Learning	—Unverified
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation	Sep 29, 2023	Federated LearningPrivacy Preserving	—Unverified
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping	Sep 29, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning	Sep 29, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—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