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 4626–4650 of 6771 papers

Title	Date	Tasks	Status
TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing	Oct 18, 2022	Explainable Artificial Intelligence (XAI)Federated Learning	—Unverified
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences	Oct 18, 2022	Federated LearningStochastic Optimization	—Unverified
Random Orthogonalization for Federated Learning in Massive MIMO Systems	Oct 18, 2022	Federated Learning	—Unverified
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning	Oct 17, 2022	Federated Learningimage-classification	CodeCode Available
Industry-Scale Orchestrated Federated Learning for Drug Discovery	Oct 17, 2022	Drug DiscoveryFederated Learning	—Unverified
Data Subsampling for Bayesian Neural Networks	Oct 17, 2022	Bayesian InferenceFederated Learning	CodeCode Available
Federated Learning with Privacy-Preserving Ensemble Attention Distillation	Oct 16, 2022	Federated Learningimage-classification	—Unverified
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation	Oct 15, 2022	Federated Learning	—Unverified
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability	Oct 15, 2022	Federated Learning	—Unverified
CrowdGuard: Federated Backdoor Detection in Federated Learning	Oct 14, 2022	Federated LearningOutlier Detection	CodeCode Available
FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning	Oct 14, 2022	Federated Learning	—Unverified
Toward Secure and Private Over-the-Air Federated Learning	Oct 14, 2022	Federated LearningScheduling	—Unverified
A Primal-Dual Algorithm for Hybrid Federated Learning	Oct 14, 2022	Federated Learning	—Unverified
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation	Oct 13, 2022	Federated Learning	—Unverified
ScionFL: Efficient and Robust Secure Quantized Aggregation	Oct 13, 2022	Federated LearningQuantization	—Unverified
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning	Oct 13, 2022	Federated LearningReconstruction Attack	CodeCode Available
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning	Oct 13, 2022	Federated LearningMulti-agent Reinforcement Learning	—Unverified
Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning	Oct 12, 2022	Federated LearningPseudo Label	CodeCode Available
Find Your Friends: Personalized Federated Learning with the Right Collaborators	Oct 12, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Federated Continual Learning for Text Classification via Selective Inter-client Transfer	Oct 12, 2022	Continual LearningFederated Learning	CodeCode Available
Privacy of federated QR decomposition using additive secure multiparty computation	Oct 12, 2022	Federated Learning	—Unverified
Anomaly Detection via Federated Learning	Oct 12, 2022	Anomaly DetectionFederated Learning	—Unverified
Aergia: Leveraging Heterogeneity in Federated Learning Systems	Oct 12, 2022	Federated Learning	CodeCode Available
On the Performance of Gradient Tracking with Local Updates	Oct 10, 2022	Federated Learning	—Unverified
FedBA: Non-IID Federated Learning Framework in UAV Networks	Oct 10, 2022	Federated Learning	—Unverified

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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