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 1576–1600 of 6771 papers

Title	Date	Tasks	Status	Hype
FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication	Jul 31, 2024	Federated LearningSemantic Communication	—Unverified	0
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights	Jul 31, 2024	Activity RecognitionFederated Learning	CodeCode Available	0
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning	Jul 30, 2024	Federated Learning	—Unverified	0
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing	Jul 30, 2024	Federated LearningPrivacy Preserving	—Unverified	0
UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks	Jul 29, 2024	Federated Learningimage-classification	CodeCode Available	0
Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction	Jul 29, 2024	Federated LearningPrivacy Preserving	—Unverified	0
F-KANs: Federated Kolmogorov-Arnold Networks	Jul 29, 2024	ClassificationFederated Learning	CodeCode Available	0
FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models	Jul 29, 2024	Federated Learning	—Unverified	0
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction	Jul 28, 2024	Federated Learning	CodeCode Available	1
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain	Jul 28, 2024	Autonomous DrivingDeep Reinforcement Learning	—Unverified	0
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps	Jul 27, 2024	Federated Learning	—Unverified	0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering	Jul 27, 2024	ClusteringFairness	—Unverified	0
A collaborative ensemble construction method for federated random forest	Jul 27, 2024	Federated Learning	—Unverified	0
Reducing Spurious Correlation for Federated Domain Generalization	Jul 27, 2024	Domain GeneralizationFederated Learning	—Unverified	0
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification	Jul 26, 2024	Federated LearningPrivacy Preserving	—Unverified	0
FLUE: Federated Learning with Un-Encrypted model weights	Jul 26, 2024	Federated LearningFLUE	—Unverified	0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction	Jul 26, 2024	Click-Through Rate PredictionFederated Learning	—Unverified	0
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning	Jul 26, 2024	Federated LearningInference Attack	—Unverified	0
FADAS: Towards Federated Adaptive Asynchronous Optimization	Jul 25, 2024	Federated LearningPrivacy Preserving	CodeCode Available	0
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future	Jul 25, 2024	Federated Learning	—Unverified	0
SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment	Jul 25, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Sparse Incremental Aggregation in Multi-Hop Federated Learning	Jul 25, 2024	Federated Learning	—Unverified	0
Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review	Jul 25, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets	Jul 25, 2024	Federated Learning	—Unverified	0
HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging	Jul 25, 2024	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