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 1401–1425 of 6771 papers

Title	Date	Tasks	Status	Score
Data Subsampling for Bayesian Neural Networks	Oct 17, 2022	Bayesian InferenceFederated Learning	CodeCode Available	5
Federated Continual Learning for Text Classification via Selective Inter-client Transfer	Oct 12, 2022	Continual LearningFederated Learning	CodeCode Available	5
A Federated Learning Benchmark for Drug-Target Interaction	Feb 15, 2023	Federated LearningPrivacy Preserving	CodeCode Available	5
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data	Oct 28, 2024	Federated Learning	CodeCode Available	5
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning	May 24, 2024	counterfactualDecision Making	CodeCode Available	5
FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissions	Apr 25, 2024	Contrastive LearningFederated Learning	CodeCode Available	5
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data	Jul 14, 2021	Federated Learning	CodeCode Available	5
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment	May 24, 2022	Federated Learning	CodeCode Available	5
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates	Jun 8, 2023	Federated Learning	CodeCode Available	5
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning	Jul 21, 2021	Federated Learning	CodeCode Available	5
Federated Active Learning for Target Domain Generalisation	Dec 4, 2023	Active LearningFederated Learning	CodeCode Available	5
Federated Black-Box Adaptation for Semantic Segmentation	Oct 31, 2024	Federated LearningSemantic Segmentation	CodeCode Available	5
A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning	Oct 28, 2024	Federated Learning	CodeCode Available	5
Federated Graph Learning with Structure Proxy Alignment	Aug 18, 2024	Federated LearningFraud Detection	CodeCode Available	5
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning	Dec 5, 2024	Federated Learningimage-classification	CodeCode Available	5
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels	Dec 19, 2023	Federated LearningLearning with noisy labels	CodeCode Available	5
AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learning	Jan 8, 2025	Federated Learning	CodeCode Available	5
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment	May 10, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Communication-Efficient Federated Learning via Predictive Coding	Aug 2, 2021	Data CompressionFederated Learning	CodeCode Available	5
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit	May 6, 2023	Federated LearningScene Classification	CodeCode Available	5
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning	Dec 5, 2024	Federated LearningKnowledge Distillation	CodeCode Available	5
Financial Data Analysis with Robust Federated Logistic Regression	Apr 28, 2025	Federated LearningMulti-class Classification	CodeCode Available	5
FedDebug: Systematic Debugging for Federated Learning Applications	Jan 9, 2023	Fault localizationFederated Learning	CodeCode Available	5
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification	Feb 24, 2023	Federated Learning	CodeCode Available	5
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement	Apr 7, 2022	Federated Learning	CodeCode Available	5

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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