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 501–525 of 6771 papers

Title	Date	Tasks	Status	Hype
Personalized Federated Learning With Graph	Mar 2, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving	Feb 28, 2022	Autonomous DrivingDomain Generalization	CodeCode Available	1
Sky Computing: Accelerating Geo-distributed Computing in Federated Learning	Feb 24, 2022	Distributed ComputingFederated Learning	CodeCode Available	1
Privacy Leakage of Adversarial Training Models in Federated Learning Systems	Feb 21, 2022	Federated Learning	CodeCode Available	1
LAMP: Extracting Text from Gradients with Language Model Priors	Feb 17, 2022	Federated LearningLanguage Modeling	CodeCode Available	1
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification	Feb 15, 2022	Federated LearningInference Attack	CodeCode Available	1
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT	Feb 15, 2022	Deep Reinforcement LearningEdge-computing	CodeCode Available	1
UA-FedRec: Untargeted Attack on Federated News Recommendation	Feb 14, 2022	Federated LearningNews Recommendation	CodeCode Available	1
Federated Learning of Generative Image Priors for MRI Reconstruction	Feb 8, 2022	Federated LearningMRI Reconstruction	CodeCode Available	1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning	Feb 8, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
FL_PyTorch: optimization research simulator for federated learning	Feb 7, 2022	Federated LearningGPU	CodeCode Available	1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning	Feb 6, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning	Feb 5, 2022	Emotion RecognitionFederated Learning	CodeCode Available	1
Proportional Fairness in Federated Learning	Feb 3, 2022	FairnessFederated Learning	CodeCode Available	1
Byzantine-Robust Decentralized Learning via ClippedGossip	Feb 3, 2022	Federated Learning	CodeCode Available	1
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?	Feb 1, 2022	Distributed OptimizationFederated Learning	CodeCode Available	1
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks	Jan 28, 2022	Federated LearningNode Classification	CodeCode Available	1
Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients	Jan 26, 2022	Federated LearningImage Classification	CodeCode Available	1
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning	Jan 24, 2022	Federated LearningSecond-order methods	CodeCode Available	1
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis	Jan 22, 2022	Federated LearningImage Generation	CodeCode Available	1
Communication-Efficient Federated Learning with Accelerated Client Gradient	Jan 10, 2022	Federated Learning	CodeCode Available	1
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients	Jan 5, 2022	Federated Learning	CodeCode Available	1
Robust Federated Learning With Noisy and Heterogeneous Clients	Jan 1, 2022	Federated Learning	CodeCode Available	1
Learn From Others and Be Yourself in Heterogeneous Federated Learning	Jan 1, 2022	Continual LearningFederated Learning	CodeCode Available	1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings	Dec 26, 2021	AttributeEmotion Recognition	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