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 4776–4800 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy	Apr 22, 2022	Data PoisoningFederated Learning	—Unverified	0
A Closer Look at Personalization in Federated Image Classification	Apr 22, 2022	ClassificationEdge-computing	—Unverified	0
Federated Learning Enables Big Data for Rare Cancer Boundary Detection	Apr 22, 2022	Boundary DetectionFederated Learning	CodeCode Available	1
Federated Learning via Inexact ADMM	Apr 22, 2022	Federated Learning	CodeCode Available	1
Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model	Apr 22, 2022	Binary ClassificationFederated Learning	—Unverified	0
Efficient Wireless Federated Learning with Partial Model Aggregation	Apr 20, 2022	Federated Learningmodel	—Unverified	0
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database	Apr 20, 2022	Federated Learning	—Unverified	0
Is Non-IID Data a Threat in Federated Online Learning to Rank?	Apr 20, 2022	Federated LearningInformation Retrieval	CodeCode Available	0
Federated Learning for Distributed Energy-Efficient Resource Allocation	Apr 20, 2022	Federated LearningMulti-agent Reinforcement Learning	—Unverified	0
A Practical Cross-Device Federated Learning Framework over 5G Networks	Apr 18, 2022	Autonomous DrivingFederated Learning	—Unverified	0
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation	Apr 18, 2022	Federated LearningNews Recommendation	—Unverified	0
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence	Apr 18, 2022	Federated Learningreinforcement-learning	CodeCode Available	1
Federated Learning Cost Disparity for IoT Devices	Apr 17, 2022	Federated Learning	—Unverified	0
Self-Aware Personalized Federated Learning	Apr 17, 2022	Federated LearningPersonalized Federated Learning	—Unverified	0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup	Apr 16, 2022	Drug DiscoveryFederated Learning	—Unverified	0
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case	Apr 16, 2022	Adversarial AttackFederated Learning	—Unverified	0
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning	Apr 16, 2022	Collision AvoidanceFederated Learning	—Unverified	0
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation	Apr 16, 2022	Federated LearningImage Segmentation	CodeCode Available	1
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning	Apr 16, 2022	Federated Learning	—Unverified	0
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations	Apr 15, 2022	Federated Learning	—Unverified	0
Learning Task-Aware Energy Disaggregation: a Federated Approach	Apr 14, 2022	Federated LearningMeta-Learning	CodeCode Available	0
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation	Apr 14, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks	Apr 14, 2022	Federated Learning	CodeCode Available	0
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning	Apr 12, 2022	Federated LearningGraph Learning	—Unverified	0
Distributed learning optimisation of Cox models can leak patient data: Risks and solutions	Apr 12, 2022	Federated Learning	—Unverified	0

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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