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 5426–5450 of 6771 papers

Title	Date	Tasks	Status
When the Curious Abandon Honesty: Federated Learning Is Not Private	Dec 6, 2021	Federated LearningPrivacy Preserving	—Unverified
Intrinisic Gradient Compression for Federated Learning	Dec 5, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks	Dec 5, 2021	Federated Learning	—Unverified
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding	Dec 5, 2021	Federated Learning	—Unverified
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving	Dec 2, 2021	Autonomous DrivingAutonomous Vehicles	—Unverified
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning	Dec 2, 2021	Deep LearningFederated Learning	—Unverified
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning	Dec 2, 2021	Federated Learning	—Unverified
FedRAD: Federated Robust Adaptive Distillation	Dec 2, 2021	Federated LearningKnowledge Distillation	—Unverified
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks	Dec 2, 2021	Federated LearningIntrusion Detection	—Unverified
Context-Aware Online Client Selection for Hierarchical Federated Learning	Dec 2, 2021	Federated Learning	—Unverified
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning	Dec 1, 2021	Federated Learning	—Unverified
Public Data-Assisted Mirror Descent for Private Model Training	Dec 1, 2021	Federated Learning	—Unverified
Boosting with Multiple Sources	Dec 1, 2021	Federated Learning	—Unverified
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data	Dec 1, 2021	Federated Learning	—Unverified
Models of fairness in federated learning	Dec 1, 2021	FairnessFederated Learning	—Unverified
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning	Dec 1, 2021	BIG-bench Machine LearningFairness	—Unverified
Breaking the centralized barrier for cross-device federated learning	Dec 1, 2021	Federated Learning	—Unverified
IndicFed: A Federated Approach for Sentiment Analysis in Indic Languages	Dec 1, 2021	Federated LearningPrivacy Preserving	—Unverified
Improving Differentially Private SGD via Randomly Sparsified Gradients	Dec 1, 2021	Federated Learning	CodeCode Available
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization	Dec 1, 2021	Federated Learningmedical image detection	—Unverified
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	Dec 1, 2021	COVID-19 DiagnosisFederated Learning	—Unverified
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization	Dec 1, 2021	Federated Learning	—Unverified
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries	Dec 1, 2021	Federated Learning	—Unverified
Federated-EM with heterogeneity mitigation and variance reduction	Dec 1, 2021	Federated LearningImputation	—Unverified
Communication-Efficient Federated Learning via Quantized Compressed Sensing	Nov 30, 2021	compressed sensingFederated Learning	—Unverified

Show:10 25 50

← PrevPage 218 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