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 4651–4675 of 6771 papers

Title	Date	Tasks	Status	Hype
A Unified Analysis of Federated Learning with Arbitrary Client Participation	May 26, 2022	Federated Learning	—Unverified	0
Cali3F: Calibrated Fast Fair Federated Recommendation System	May 26, 2022	FairnessFederated Learning	—Unverified	0
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information	May 26, 2022	Federated Learningtext-classification	—Unverified	0
Federated Split BERT for Heterogeneous Text Classification	May 26, 2022	ClassificationFederated Learning	—Unverified	0
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning	May 26, 2022	Federated Learning	—Unverified	0
A Fair Federated Learning Framework With Reinforcement Learning	May 26, 2022	FairnessFederated Learning	—Unverified	0
QUIC-FL: Quick Unbiased Compression for Federated Learning	May 26, 2022	Federated LearningQuantization	—Unverified	0
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction	May 26, 2022	Domain GeneralizationFederated Learning	CodeCode Available	1
Combating Client Dropout in Federated Learning via Friend Model Substitution	May 26, 2022	Federated Learning	—Unverified	0
Federated Adaptation of Reservoirs via Intrinsic Plasticity	May 25, 2022	Federated Learning	—Unverified	0
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking	May 25, 2022	Federated Learning	—Unverified	0
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers	May 25, 2022	Federated LearningPosition	CodeCode Available	1
VeriFi: Towards Verifiable Federated Unlearning	May 25, 2022	Federated Learning	—Unverified	0
Federated Self-supervised Learning for Heterogeneous Clients	May 25, 2022	Federated LearningRepresentation Learning	—Unverified	0
Differentially Private AUC Computation in Vertical Federated Learning	May 24, 2022	Federated LearningVertical Federated Learning	—Unverified	0
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment	May 24, 2022	Federated Learning	CodeCode Available	0
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees	May 24, 2022	Federated Learning	CodeCode Available	1
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training	May 24, 2022	Continual LearningFederated Learning	CodeCode Available	1
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning	May 24, 2022	Autonomous VehiclesBIG-bench Machine Learning	—Unverified	0
Federated singular value decomposition for high dimensional data	May 24, 2022	Federated LearningVocal Bursts Intensity Prediction	CodeCode Available	0
Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis	May 24, 2022	BenchmarkingFederated Learning	CodeCode Available	1
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning	May 23, 2022	AttributeDecision Making	—Unverified	0
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing	May 23, 2022	Federated LearningIntrusion Detection	—Unverified	0
Semi-Decentralized Federated Learning with Collaborative Relaying	May 23, 2022	Federated Learning	—Unverified	0
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation	May 23, 2022	Data ValuationFederated 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