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 6376–6400 of 6771 papers

Title	Date	Tasks	Status
Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models	May 16, 2024	Federated Learning	CodeCode Available
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection	Jul 11, 2025	Computational EfficiencyFederated Learning	CodeCode Available
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks	Apr 22, 2024	Federated Learning	CodeCode Available
Bayesian Robust Aggregation for Federated Learning	May 5, 2025	Bayesian InferenceFederated Learning	CodeCode Available
PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning	Feb 12, 2025	Federated LearningSensitivity	CodeCode Available
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems	Jun 9, 2025	Federated LearningPrivacy Preserving	CodeCode Available
Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging	Jul 2, 2024	Federated Learning	CodeCode Available
Federated Learning for Keyword Spotting	Oct 9, 2018	Federated LearningKeyword Spotting	CodeCode Available
Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning	Jan 1, 2025	Federated LearningTransfer Learning	CodeCode Available
Federated Learning for Data Streams	Jan 4, 2023	Federated Learning	CodeCode Available
Empowering Data Mesh with Federated Learning	Mar 26, 2024	Decision MakingFederated Learning	CodeCode Available
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring Plugin	Nov 15, 2024	Federated Learningimage-classification	CodeCode Available
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data	Mar 16, 2023	Federated LearningGraph Neural Network	CodeCode Available
Federated learning compression designed for lightweight communications	Oct 23, 2023	Cloud ComputingFederated Learning	CodeCode Available
Global Balanced Experts for Federated Long-Tailed Learning	Jan 1, 2023	Federated LearningPrivacy Preserving	CodeCode Available
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records	Dec 10, 2021	Federated LearningMeta-Learning	CodeCode Available
Global Group Fairness in Federated Learning via Function Tracking	Mar 19, 2025	FairnessFederated Learning	CodeCode Available
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications	Jul 2, 2019	Data AugmentationEdge-computing	CodeCode Available
Cross-silo Federated Learning with Record-level Personalized Differential Privacy	Jan 29, 2024	Federated Learning	CodeCode Available
Global Layers: Non-IID Tabular Federated Learning	May 29, 2023	Federated Learning	CodeCode Available
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence	Jul 20, 2024	Federated Learning	CodeCode Available
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment	Nov 8, 2023	FairnessFederated Learning	CodeCode Available
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems	Jul 7, 2020	ClusteringFederated Learning	CodeCode Available
GLow -- A Novel, Flower-Based Simulated Gossip Learning Strategy	Jan 15, 2025	Federated Learning	CodeCode Available
Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology	Jul 20, 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