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 801–825 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection	Jun 15, 2021	Anomaly DetectionFederated Learning	CodeCode Available	1	5
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning	Feb 6, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1	5
Rethinking Federated Learning With Domain Shift: A Prototype View	Jan 1, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Revisiting Weighted Aggregation in Federated Learning with Neural Networks	Feb 14, 2023	Federated Learning	CodeCode Available	1	5
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease	Jul 7, 2021	Action RecognitionData Augmentation	CodeCode Available	1	5
Benchmarking Algorithms for Federated Domain Generalization	Jul 11, 2023	BenchmarkingDiversity	CodeCode Available	1	5
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS	Apr 1, 2022	Autonomous VehiclesFederated Learning	CodeCode Available	1	5
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients	Apr 7, 2022	Federated Learning	CodeCode Available	1	5
Benchmarking Differential Privacy and Federated Learning for BERT Models	Jun 26, 2021	BenchmarkingFederated Learning	CodeCode Available	1	5
Robust Federated Learning with Noisy Labels	Dec 3, 2020	Federated LearningLearning with noisy labels	CodeCode Available	1	5
LEAF: A Benchmark for Federated Settings	Dec 3, 2018	Autonomous VehiclesBenchmarking	CodeCode Available	1	5
Federated Learning from Small Datasets	Oct 7, 2021	Federated Learning	CodeCode Available	1	5
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model	Jun 21, 2024	Federated LearningLanguage Modeling	CodeCode Available	1	5
Salvaging Federated Learning by Local Adaptation	Feb 12, 2020	Federated LearningKnowledge Distillation	CodeCode Available	1	5
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models	Aug 26, 2020	DiversityFederated Learning	CodeCode Available	1	5
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization	May 24, 2024	ClassificationFederated Learning	CodeCode Available	1	5
Federated Learning of Gboard Language Models with Differential Privacy	May 29, 2023	Federated Learning	CodeCode Available	1	5
Federated Learning on Non-IID Data Silos: An Experimental Study	Feb 3, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1	5
Privacy-Preserving News Recommendation Model Learning	Mar 21, 2020	ArticlesFederated Learning	CodeCode Available	1	5
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization	Oct 23, 2023	Federated Learning	CodeCode Available	1	5
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning	Sep 18, 2024	Federated Learning	CodeCode Available	0	5
FedLion: Faster Adaptive Federated Optimization with Fewer Communication	Feb 15, 2024	Federated Learning	CodeCode Available	0	5
A Novel Optimized Asynchronous Federated Learning Framework	Nov 18, 2021	Federated Learning	CodeCode Available	0	5
Adaptive Guidance for Local Training in Heterogeneous Federated Learning	Oct 9, 2024	Federated Learning	CodeCode Available	0	5
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder	Jun 2, 2024	Federated LearningModel Poisoning	CodeCode Available	0	5

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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