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 6251–6275 of 6771 papers

Title	Date	Tasks	Status	Hype
POSEIDON: Privacy-Preserving Federated Neural Network Learning	Sep 1, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Federated Edge Learning : Design Issues and Challenges	Aug 31, 2020	Federated LearningScheduling	—Unverified	0
SEEC: Semantic Vector Federation across Edge Computing Environments	Aug 30, 2020	Edge-computingFederated Learning	—Unverified	0
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model	Aug 29, 2020	Dimensionality ReductionFederated Learning	—Unverified	0
A Metamodel and Framework for AGI	Aug 28, 2020	Federated Learningobject-detection	—Unverified	0
Collaborative Fairness in Federated Learning	Aug 27, 2020	Collaborative FairnessFairness	CodeCode Available	1
Performance Optimization for Federated Person Re-identification via Benchmark Analysis	Aug 26, 2020	Federated LearningKnowledge Distillation	CodeCode Available	1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models	Aug 26, 2020	DiversityFederated Learning	CodeCode Available	1
Accelerating Federated Learning in Heterogeneous Data and Computational Environments	Aug 25, 2020	Federated Learning	—Unverified	0
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations	Aug 25, 2020	Collaborative FilteringFederated Learning	—Unverified	0
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training	Aug 25, 2020	Federated LearningRepresentation Learning	CodeCode Available	0
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design	Aug 25, 2020	Deep LearningFederated Learning	—Unverified	0
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO	Aug 25, 2020	Federated Learning	—Unverified	0
Convergence of Federated Learning over a Noisy Downlink	Aug 25, 2020	Federated LearningQuantization	—Unverified	0
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning	Aug 23, 2020	Federated Learning	—Unverified	0
Federated Learning with Communication Delay in Edge Networks	Aug 21, 2020	Federated Learning	—Unverified	0
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy	Aug 21, 2020	Federated Learning	—Unverified	0
Toward Smart Security Enhancement of Federated Learning Networks	Aug 19, 2020	Deep Reinforcement LearningFederated Learning	—Unverified	0
Intelligent Radio Signal Processing: A Survey	Aug 19, 2020	Federated LearningSurvey	—Unverified	0
Shared MF: A privacy-preserving recommendation system	Aug 18, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients	Aug 18, 2020	Federated Learning	—Unverified	0
Inverse Distance Aggregation for Federated Learning with Non-IID Data	Aug 17, 2020	Federated Learning	—Unverified	0
WAFFLE: Watermarking in Federated Learning	Aug 17, 2020	Federated Learning	CodeCode Available	1
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs	Aug 17, 2020	Federated Learning	—Unverified	0
FLBench: A Benchmark Suite for Federated Learning	Aug 17, 2020	BIG-bench Machine LearningFederated Learning	—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