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 6651–6675 of 6771 papers

Title	Date	Tasks	Status
A Universal Metric of Dataset Similarity for Cross-silo Federated Learning	Apr 29, 2024	Federated LearningPrivacy Preserving	CodeCode Available
SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks	Dec 19, 2023	Federated Learning	CodeCode Available
CalFAT: Calibrated Federated Adversarial Training with Label Skewness	May 30, 2022	Adversarial RobustnessFederated Learning	CodeCode Available
Robust Aggregation for Federated Learning	Dec 31, 2019	Additive modelsFederated Learning	CodeCode Available
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement	Apr 7, 2022	Federated Learning	CodeCode Available
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting	Feb 2, 2024	Federated LearningLoad Forecasting	CodeCode Available
Robust and Communication-Efficient Federated Learning from Non-IID Data	Mar 7, 2019	Federated LearningPrivacy Preserving	CodeCode Available
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems	May 9, 2024	Federated LearningPrivacy Preserving	CodeCode Available
Robust and Communication-Efficient Federated Domain Adaptation via Random Features	Nov 8, 2023	Domain AdaptationFederated Learning	CodeCode Available
Robust and Differentially Private Mean Estimation	Feb 18, 2021	Federated LearningMeta-Learning	CodeCode Available
Anchor Sampling for Federated Learning with Partial Client Participation	Jun 13, 2022	Federated Learning	CodeCode Available
UFGraphFR: An attempt at a federated recommendation system based on user text characteristics	Jan 14, 2025	AttributeFederated Learning	CodeCode Available
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients	Mar 12, 2025	Contrastive LearningData Augmentation	CodeCode Available
On Noisy Evaluation in Federated Hyperparameter Tuning	Dec 17, 2022	Federated Learning	CodeCode Available
On the Importance and Applicability of Pre-Training for Federated Learning	Jun 23, 2022	Federated Learning	CodeCode Available
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization	Feb 21, 2023	Federated LearningKnowledge Distillation	CodeCode Available
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning	Feb 8, 2025	Anomaly DetectionFederated Learning	CodeCode Available
In-depth Analysis of Privacy Threats in Federated Learning for Medical Data	Sep 27, 2024	Federated Learning	CodeCode Available
Towards the efficacy of federated prediction for epidemics on networks	Dec 3, 2024	Federated LearningGraph Attention	CodeCode Available
Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation	Jul 17, 2024	Federated LearningPrediction	CodeCode Available
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation	Jun 18, 2024	Federated Learningimage-classification	CodeCode Available
On the Byzantine-Resilience of Distillation-Based Federated Learning	Feb 19, 2024	Federated LearningKnowledge Distillation	CodeCode Available
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects	Jan 1, 2023	Causal InferenceFederated Learning	CodeCode Available
A Federated Learning Benchmark for Drug-Target Interaction	Feb 15, 2023	Federated LearningPrivacy Preserving	CodeCode Available
On the Convergence of Clustered Federated Learning	Feb 13, 2022	Federated Learning	CodeCode Available

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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