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 376–400 of 6771 papers

Title	Date	Tasks	Status	Hype
FedLesScan: Mitigating Stragglers in Serverless Federated Learning	Nov 10, 2022	Federated Learning	CodeCode Available	1
Enhancing Efficiency in Multidevice Federated Learning through Data Selection	Nov 8, 2022	Federated Learning	CodeCode Available	1
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design	Nov 8, 2022	Federated Learning	CodeCode Available	1
FedTP: Federated Learning by Transformer Personalization	Nov 3, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments	Nov 1, 2022	Federated Learning	CodeCode Available	1
Fast-Convergent Federated Learning via Cyclic Aggregation	Oct 29, 2022	Federated Learning	CodeCode Available	1
Machine Unlearning of Federated Clusters	Oct 28, 2022	ClusteringFederated Learning	CodeCode Available	1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks	Oct 25, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication	Oct 25, 2022	Federated Learningimage-classification	CodeCode Available	1
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning	Oct 23, 2022	Backdoor Attackbackdoor defense	CodeCode Available	1
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning	Oct 19, 2022	Federated Learning	CodeCode Available	1
FedForgery: Generalized Face Forgery Detection with Residual Federated Learning	Oct 18, 2022	Federated LearningImage Generation	CodeCode Available	1
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning	Oct 14, 2022	Federated Learning	CodeCode Available	1
Label Inference Attacks Against Vertical Federated Learning	Oct 10, 2022	Federated LearningVertical Federated Learning	CodeCode Available	1
Few-Shot Model Agnostic Federated Learning	Oct 10, 2022	Federated Learningmodel	CodeCode Available	1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning	Oct 6, 2022	Federated Learning	CodeCode Available	1
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning	Oct 5, 2022	Autonomous DrivingDomain Adaptation	CodeCode Available	1
TabLeak: Tabular Data Leakage in Federated Learning	Oct 4, 2022	Federated LearningReconstruction Attack	CodeCode Available	1
Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer	Oct 3, 2022	Domain GeneralizationFederated Learning	CodeCode Available	1
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning	Oct 1, 2022	Federated Learning	CodeCode Available	1
Sparse Random Networks for Communication-Efficient Federated Learning	Sep 30, 2022	Federated Learning	CodeCode Available	1
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks	Sep 30, 2022	Federated Learning	CodeCode Available	1
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces	Sep 21, 2022	Federated Learning	CodeCode Available	1
Characterizing Internal Evasion Attacks in Federated Learning	Sep 17, 2022	Adversarial RobustnessFederated Learning	CodeCode Available	1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning	Sep 12, 2022	Federated Learningimage-classification	CodeCode Available	1

Show:10 25 50

← PrevPage 16 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