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 6276–6300 of 6771 papers

Title	Date	Tasks	Status	Hype
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank	Aug 17, 2020	Federated LearningLearning-To-Rank	—Unverified	0
Siloed Federated Learning for Multi-Centric Histopathology Datasets	Aug 17, 2020	Deep LearningDomain Adaptation	—Unverified	0
Heterogeneous Federated Learning	Aug 15, 2020	Federated Learning	—Unverified	0
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data	Aug 14, 2020	Data AugmentationFederated Learning	—Unverified	0
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data	Aug 14, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning	Aug 14, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Privacy Preserving Vertical Federated Learning for Tree-based Models	Aug 14, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Addressing Class Imbalance in Federated Learning	Aug 14, 2020	Federated Learning	CodeCode Available	1
WAFFLe: Weight Anonymized Factorization for Federated Learning	Aug 13, 2020	FairnessFederated Learning	—Unverified	0
Distantly Supervised Relation Extraction in Federated Settings	Aug 12, 2020	DenoisingFederated Learning	CodeCode Available	1
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching	Aug 11, 2020	Federated Learning	—Unverified	0
FedNNNN: Norm-Normalized Neural Network Aggregation for Fast and Accurate Federated Learning	Aug 11, 2020	Federated Learning	—Unverified	0
Federated Learning via Synthetic Data	Aug 11, 2020	Federated Learning	—Unverified	0
Holdout SGD: Byzantine Tolerant Federated Learning	Aug 11, 2020	Federated Learning	—Unverified	0
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning	Aug 8, 2020	Federated Learning	CodeCode Available	1
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets	Aug 7, 2020	Federated Learning	CodeCode Available	1
Federated Transfer Learning with Dynamic Gradient Aggregation	Aug 6, 2020	Federated Learningspeech-recognition	—Unverified	0
Improving on-device speaker verification using federated learning with privacy	Aug 6, 2020	Federated LearningMulti-Task Learning	—Unverified	0
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI	Aug 4, 2020	ClusteringFederated Learning	—Unverified	0
State-of-the-art Techniques in Deep Edge Intelligence	Aug 3, 2020	Deep LearningEdge-computing	—Unverified	0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning	Aug 3, 2020	DecoderDiversity	—Unverified	0
The Need for Advanced Intelligence in NFV Management and Orchestration	Aug 3, 2020	Federated LearningManagement	—Unverified	0
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization	Aug 1, 2020	Federated Learning	—Unverified	0
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy	Jul 31, 2020	Federated Learning	—Unverified	0
Communication-Efficient Federated Learning via Optimal Client Sampling	Jul 30, 2020	Federated LearningLanguage Modeling	—Unverified	0

Show:10 25 50

← PrevPage 252 of 271Next →

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