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 5526–5550 of 6771 papers

Title	Date	Tasks	Status
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices	Nov 1, 2021	Edge-computingFederated Learning	—Unverified
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	Oct 31, 2021	Federated LearningPrivacy Preserving	—Unverified
Revealing and Protecting Labels in Distributed Training	Oct 31, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design	Oct 31, 2021	Federated LearningScheduling	—Unverified
Dynamic Differential-Privacy Preserving SGD	Oct 30, 2021	Federated Learningimage-classification	—Unverified
Federated Semi-Supervised Learning with Class Distribution Mismatch	Oct 29, 2021	Federated Learning	—Unverified
Towards Model Agnostic Federated Learning Using Knowledge Distillation	Oct 28, 2021	Federated LearningKnowledge Distillation	—Unverified
Federated Learning with Heterogeneous Differential Privacy	Oct 28, 2021	Federated LearningPrivacy Preserving	—Unverified
Communication-Efficient ADMM-based Federated Learning	Oct 28, 2021	Federated Learning	CodeCode Available
FedPrune: Towards Inclusive Federated Learning	Oct 27, 2021	FairnessFederated Learning	—Unverified
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation	Oct 27, 2021	Federated Learning	—Unverified
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks	Oct 27, 2021	Federated Learning	—Unverified
What Do We Mean by Generalization in Federated Learning?	Oct 27, 2021	Federated Learning	—Unverified
Differentially Private Federated Bayesian Optimization with Distributed Exploration	Oct 27, 2021	Bayesian OptimizationFederated Learning	—Unverified
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design	Oct 26, 2021	Federated Learning	—Unverified
MarS-FL: Enabling Competitors to Collaborate in Federated Learning	Oct 26, 2021	Decision MakingFederated Learning	—Unverified
DPCOVID: Privacy-Preserving Federated Covid-19 Detection	Oct 26, 2021	Federated LearningPrivacy Preserving	—Unverified
Ensemble Federated Adversarial Training with Non-IID data	Oct 26, 2021	DiversityFederated Learning	—Unverified
Optimal Model Averaging: Towards Personalized Collaborative Learning	Oct 25, 2021	Federated Learningmodel	—Unverified
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation	Oct 25, 2021	Face Presentation Attack DetectionFace Recognition	—Unverified
Optimization-Based GenQSGD for Federated Edge Learning	Oct 25, 2021	Edge-computingFederated Learning	—Unverified
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks	Oct 23, 2021	ClassificationFederated Learning	—Unverified
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning	Oct 23, 2021	Federated Learningimage-classification	CodeCode Available
Federated Unlearning via Class-Discriminative Pruning	Oct 22, 2021	Federated Learningimage-classification	CodeCode Available
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources	Oct 22, 2021	Federated LearningScheduling	—Unverified

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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