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 6151–6175 of 6771 papers

Title	Date	Tasks	Status	Hype
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee	Nov 3, 2020	Distributed ComputingFairness	—Unverified	0
Semi-supervised Federated Learning for Activity Recognition	Nov 2, 2020	Activity RecognitionData Augmentation	—Unverified	0
Empirical Studies of Institutional Federated Learning For Natural Language Processing	Nov 1, 2020	Federated LearningGPU	—Unverified	0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction	Nov 1, 2020	Federated LearningKnowledge Distillation	—Unverified	0
One-Shot Federated Learning with Neuromorphic Processors	Nov 1, 2020	Federated LearningGesture Recognition	—Unverified	0
Fast Convergence Algorithm for Analog Federated Learning	Oct 30, 2020	Federated Learning	—Unverified	0
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework	Oct 29, 2020	Federated Learningspeech-recognition	—Unverified	0
Mitigating Backdoor Attacks in Federated Learning	Oct 28, 2020	Federated Learning	—Unverified	0
Federated Learning From Big Data Over Networks	Oct 27, 2020	Federated Learningregression	CodeCode Available	0
Spiking Neural Networks -- Part III: Neuromorphic Communications	Oct 27, 2020	Federated Learning	—Unverified	0
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition	Oct 26, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
Optimal Client Sampling for Federated Learning	Oct 26, 2020	Federated Learning	CodeCode Available	1
Optimal Importance Sampling for Federated Learning	Oct 26, 2020	Federated Learningregression	—Unverified	0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms	Oct 25, 2020	Distributed OptimizationFederated Learning	—Unverified	0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things	Oct 25, 2020	ClusteringDeep Reinforcement Learning	—Unverified	0
Federated Bandit: A Gossiping Approach	Oct 24, 2020	Federated Learning	—Unverified	0
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD	Oct 24, 2020	Federated Learning	CodeCode Available	0
Throughput-Optimal Topology Design for Cross-Silo Federated Learning	Oct 23, 2020	Federated Learning	CodeCode Available	1
Network Anomaly Detection Using Federated Learning and Transfer Learning	Oct 22, 2020	Anomaly DetectionFederated Learning	—Unverified	0
Hierarchical Federated Learning through LAN-WAN Orchestration	Oct 22, 2020	Federated Learning	—Unverified	0
Differentially-Private Federated Linear Bandits	Oct 22, 2020	Federated Learning	CodeCode Available	0
GFL: A Decentralized Federated Learning Framework Based On Blockchain	Oct 21, 2020	Data PoisoningFederated Learning	—Unverified	0
Feature Inference Attack on Model Predictions in Vertical Federated Learning	Oct 20, 2020	Federated LearningInference Attack	CodeCode Available	1
A Federated Learning Approach to Anomaly Detection in Smart Buildings	Oct 20, 2020	Anomaly DetectionFederated Learning	—Unverified	0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation	Oct 20, 2020	Federated LearningKnowledge Distillation	—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