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 5126–5150 of 6771 papers

Title	Date	Tasks	Status	Hype
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating	Dec 11, 2021	Federated Learning	—Unverified	0
Efficient Device Scheduling with Multi-Job Federated Learning	Dec 11, 2021	Bayesian OptimizationFairness	—Unverified	0
Sequence-level self-learning with multiple hypotheses	Dec 10, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records	Dec 10, 2021	Federated LearningMeta-Learning	CodeCode Available	0
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning	Dec 10, 2021	Federated LearningInference Attack	—Unverified	0
Federated Two-stage Learning with Sign-based Voting	Dec 10, 2021	BIG-bench Machine LearningFederated Learning	—Unverified	0
Specificity-Preserving Federated Learning for MR Image Reconstruction	Dec 9, 2021	Federated LearningImage Reconstruction	CodeCode Available	1
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost	Dec 8, 2021	Federated LearningQuantization	—Unverified	0
Location Leakage in Federated Signal Maps	Dec 7, 2021	Federated Learning	—Unverified	0
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks	Dec 7, 2021	Autonomous VehiclesDeep Reinforcement Learning	CodeCode Available	1
When the Curious Abandon Honesty: Federated Learning Is Not Private	Dec 6, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding	Dec 5, 2021	Federated Learning	—Unverified	0
Intrinisic Gradient Compression for Federated Learning	Dec 5, 2021	BIG-bench Machine LearningFederated Learning	—Unverified	0
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks	Dec 5, 2021	Federated Learning	—Unverified	0
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning	Dec 2, 2021	Deep LearningFederated Learning	—Unverified	0
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks	Dec 2, 2021	Federated LearningIntrusion Detection	—Unverified	0
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning	Dec 2, 2021	Federated Learning	—Unverified	0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving	Dec 2, 2021	Autonomous DrivingAutonomous Vehicles	—Unverified	0
FedRAD: Federated Robust Adaptive Distillation	Dec 2, 2021	Federated LearningKnowledge Distillation	—Unverified	0
Context-Aware Online Client Selection for Hierarchical Federated Learning	Dec 2, 2021	Federated Learning	—Unverified	0
Projected Federated Averaging with Heterogeneous Differential Privacy	Dec 1, 2021	Federated Learning	CodeCode Available	1
IndicFed: A Federated Approach for Sentiment Analysis in Indic Languages	Dec 1, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Models of fairness in federated learning	Dec 1, 2021	FairnessFederated Learning	—Unverified	0
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data	Dec 1, 2021	Federated Learning	—Unverified	0
Improving Differentially Private SGD via Randomly Sparsified Gradients	Dec 1, 2021	Federated Learning	CodeCode Available	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