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 676–700 of 6771 papers

Title	Date	Tasks	Status	Hype
Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems	Feb 19, 2021	Federated Learning	CodeCode Available	1
Label Leakage and Protection in Two-party Split Learning	Feb 17, 2021	Federated LearningVocal Bursts Valence Prediction	CodeCode Available	1
A Federated Data-Driven Evolutionary Algorithm	Feb 16, 2021	Federated LearningManagement	CodeCode Available	1
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization	Feb 15, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1
Exploiting Shared Representations for Personalized Federated Learning	Feb 14, 2021	Federated LearningMeta-Learning	CodeCode Available	1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization	Feb 14, 2021	Federated LearningFew-Shot Learning	CodeCode Available	1
Robust Federated Learning with Attack-Adaptive Aggregation	Feb 10, 2021	Federated LearningModel Poisoning	CodeCode Available	1
CaPC Learning: Confidential and Private Collaborative Learning	Feb 9, 2021	FairnessFederated Learning	CodeCode Available	1
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach	Feb 9, 2021	Federated Learning	CodeCode Available	1
FedMood: Federated Learning on Mobile Health Data for Mood Detection	Feb 6, 2021	BIG-bench Machine LearningDepression Detection	CodeCode Available	1
Federated Reconstruction: Partially Local Federated Learning	Feb 5, 2021	Collaborative FilteringFederated Learning	CodeCode Available	1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning	Feb 4, 2021	Federated LearningUnsupervised Pre-training	CodeCode Available	1
Federated Learning on Non-IID Data Silos: An Experimental Study	Feb 3, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Federated Multi-Armed Bandits	Jan 28, 2021	Federated LearningMulti-Armed Bandits	CodeCode Available	1
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation	Jan 28, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1
Dopamine: Differentially Private Federated Learning on Medical Data	Jan 27, 2021	Federated Learning	CodeCode Available	1
Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus	Jan 9, 2021	Federated Learning	CodeCode Available	1
Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems	Jan 9, 2021	Federated Learning	CodeCode Available	1
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts	Dec 31, 2020	Decision MakingFederated Learning	CodeCode Available	1
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping	Dec 27, 2020	Federated Learning	CodeCode Available	1
Learning from History for Byzantine Robust Optimization	Dec 18, 2020	Federated LearningStochastic Optimization	CodeCode Available	1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning	Dec 16, 2020	Federated LearningIntrusion Detection	CodeCode Available	1
Personalized Federated Learning with First Order Model Optimization	Dec 15, 2020	Federated Learningmodel	CodeCode Available	1
Communication-Efficient Federated Learning with Compensated Overlap-FedAvg	Dec 12, 2020	Data CompressionFederated Learning	CodeCode Available	1
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective	Dec 8, 2020	Federated Learning	CodeCode Available	1

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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