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 5026–5050 of 6771 papers

Title	Date	Tasks	Status
Federated Split BERT for Heterogeneous Text Classification	May 26, 2022	ClassificationFederated Learning	—Unverified
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information	May 26, 2022	Federated Learningtext-classification	—Unverified
Cali3F: Calibrated Fast Fair Federated Recommendation System	May 26, 2022	FairnessFederated Learning	—Unverified
QUIC-FL: Quick Unbiased Compression for Federated Learning	May 26, 2022	Federated LearningQuantization	—Unverified
A Fair Federated Learning Framework With Reinforcement Learning	May 26, 2022	FairnessFederated Learning	—Unverified
A Unified Analysis of Federated Learning with Arbitrary Client Participation	May 26, 2022	Federated Learning	—Unverified
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking	May 25, 2022	Federated Learning	—Unverified
Federated Adaptation of Reservoirs via Intrinsic Plasticity	May 25, 2022	Federated Learning	—Unverified
Federated Self-supervised Learning for Heterogeneous Clients	May 25, 2022	Federated LearningRepresentation Learning	—Unverified
VeriFi: Towards Verifiable Federated Unlearning	May 25, 2022	Federated Learning	—Unverified
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment	May 24, 2022	Federated Learning	CodeCode Available
Federated singular value decomposition for high dimensional data	May 24, 2022	Federated LearningVocal Bursts Intensity Prediction	CodeCode Available
Differentially Private AUC Computation in Vertical Federated Learning	May 24, 2022	Federated LearningVertical Federated Learning	—Unverified
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning	May 24, 2022	Autonomous VehiclesBIG-bench Machine Learning	—Unverified
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning	May 23, 2022	AttributeDecision Making	—Unverified
Personalized Federated Learning with Server-Side Information	May 23, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing	May 23, 2022	Federated LearningIntrusion Detection	—Unverified
FL Games: A federated learning framework for distribution shifts	May 23, 2022	Federated Learning	—Unverified
CELEST: Federated Learning for Globally Coordinated Threat Detection	May 23, 2022	Active LearningFederated Learning	—Unverified
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation	May 23, 2022	Federated LearningLiver Segmentation	—Unverified
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation	May 23, 2022	Data ValuationFederated Learning	—Unverified
Federated Distillation based Indoor Localization for IoT Networks	May 23, 2022	Federated LearningIndoor Localization	—Unverified
Fed-DART and FACT: A solution for Federated Learning in a production environment	May 23, 2022	Federated Learning	—Unverified
Semi-Decentralized Federated Learning with Collaborative Relaying	May 23, 2022	Federated Learning	—Unverified
Incentivizing Federated Learning	May 22, 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