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 401–425 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS	Apr 1, 2022	Autonomous VehiclesFederated Learning	CodeCode Available	1
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients	Apr 7, 2022	Federated Learning	CodeCode Available	1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings	Dec 26, 2021	AttributeEmotion Recognition	CodeCode Available	1
Federated Learning of Gboard Language Models with Differential Privacy	May 29, 2023	Federated Learning	CodeCode Available	1
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization	Oct 23, 2023	Federated Learning	CodeCode Available	1
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features	Apr 28, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization	May 24, 2024	ClassificationFederated Learning	CodeCode Available	1
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation	Oct 29, 2019	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Learning under Heterogeneous and Correlated Client Availability	Jan 11, 2023	Federated Learning	CodeCode Available	1
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints	May 13, 2022	BenchmarkingFederated Learning	CodeCode Available	1
A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus	Apr 2, 2020	Federated Learning	CodeCode Available	1
Federated Learning via Inexact ADMM	Apr 22, 2022	Federated Learning	CodeCode Available	1
Adaptive Test-Time Personalization for Federated Learning	Oct 28, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms	Oct 11, 2020	Federated Learning	CodeCode Available	1
Federated Learning with Fair Averaging	Apr 30, 2021	FairnessFederated Learning	CodeCode Available	1
Federated Learning with Superquantile Aggregation for Heterogeneous Data	Dec 17, 2021	Federated Learning	CodeCode Available	1
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated Learning	CodeCode Available	1
Federated Learning with Matched Averaging	Feb 15, 2020	Federated Learning	CodeCode Available	1
Federated Learning with Positive and Unlabeled Data	Jun 21, 2021	Federated Learning	CodeCode Available	1
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML	Sep 14, 2024	Federated LearningPrivacy Preserving	CodeCode Available	1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning	Oct 15, 2021	Federated Learning	CodeCode Available	1
Federated Learning with Spiking Neural Networks	Jun 11, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage	Mar 29, 2022	Bayesian OptimizationFederated Learning	CodeCode Available	1
Federated Multi-Armed Bandits	Jan 28, 2021	Federated LearningMulti-Armed Bandits	CodeCode Available	1
Asynchronous Federated Learning for Edge-assisted Vehicular Networks	Aug 3, 2022	Federated Learning	CodeCode Available	1

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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