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	Score
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing	Oct 14, 2021	Cloud ComputingFederated Learning	CodeCode Available	1	5
Communication-Efficient Learning of Deep Networks from Decentralized Data	Feb 17, 2016	Federated LearningSpeech Recognition	CodeCode Available	1	5
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection	Jun 15, 2021	Anomaly DetectionFederated Learning	CodeCode Available	1	5
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS	Apr 1, 2022	Autonomous VehiclesFederated Learning	CodeCode Available	1	5
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients	Apr 7, 2022	Federated Learning	CodeCode Available	1	5
Applied Federated Learning: Improving Google Keyboard Query Suggestions	Dec 7, 2018	BIG-bench Machine LearningFederated Learning	CodeCode Available	1	5
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method	Sep 29, 2024	Federated LearningLearning Theory	CodeCode Available	1	5
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance	Jun 27, 2022	Federated Learning	CodeCode Available	1	5
Flame: Simplifying Topology Extension in Federated Learning	May 9, 2023	Federated Learning	CodeCode Available	1	5
Continual Local Training for Better Initialization of Federated Models	May 26, 2020	Federated Learning	CodeCode Available	1	5
Federated Learning under Distributed Concept Drift	Jun 1, 2022	ClusteringDrift Detection	CodeCode Available	1	5
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning	Dec 16, 2020	Federated LearningIntrusion Detection	CodeCode Available	1	5
Specialized federated learning using a mixture of experts	Oct 5, 2020	Federated LearningMixture-of-Experts	CodeCode Available	1	5
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection	Feb 7, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Proportional Fairness in Federated Learning	Feb 3, 2022	FairnessFederated Learning	CodeCode Available	1	5
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations	Aug 21, 2023	Federated Learning	CodeCode Available	1	5
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	Jun 15, 2021	Federated Learning	CodeCode Available	1	5
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning	Jun 19, 2020	Federated LearningStochastic Optimization	CodeCode Available	1	5
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning	Nov 15, 2022	Federated Learningimage-classification	CodeCode Available	1	5
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data	Aug 7, 2023	Contrastive LearningFederated Learning	CodeCode Available	1	5
DAGER: Exact Gradient Inversion for Large Language Models	May 24, 2024	DecoderFederated Learning	CodeCode Available	1	5
Exploiting Label Skews in Federated Learning with Model Concatenation	Dec 11, 2023	Federated Learningimage-classification	CodeCode Available	1	5
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks	Nov 28, 2023	Federated LearningImage Generation	CodeCode Available	1	5
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices	Dec 8, 2024	Edge-computingFederated Learning	CodeCode Available	1	5
A Dynamic Weighted Federated Learning for Android Malware Classification	Nov 23, 2022	ClassificationFederated Learning	CodeCode Available	1	5

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