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 951–975 of 6771 papers

Title	Date	Tasks	Status
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning	Nov 13, 2021	Anomaly DetectionFederated Learning	—Unverified
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance	Apr 19, 2024	Federated Learning	—Unverified
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach	Sep 22, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning	May 28, 2025	FairnessFederated Learning	—Unverified
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning	Dec 28, 2024	Federated Learning	—Unverified
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers	Nov 6, 2023	Federated Learning	—Unverified
CAFE: Catastrophic Data Leakage in Federated Learning	Jan 1, 2021	Federated Learning	—Unverified
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning	Jun 21, 2022	Federated Learning	—Unverified
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments	Oct 21, 2021	Federated LearningTransfer Learning	—Unverified
CAFe: Cost and Age aware Federated Learning	May 24, 2024	Federated Learning	—Unverified
Cali3F: Calibrated Fast Fair Federated Recommendation System	May 26, 2022	FairnessFederated Learning	—Unverified
An Information Theoretic Perspective on Conformal Prediction	May 3, 2024	Conformal PredictionFederated Learning	—Unverified
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering	Aug 22, 2022	ClusteringFederated Learning	—Unverified
Can collaborative learning be private, robust and scalable?	May 5, 2022	Adversarial RobustnessFederated Learning	—Unverified
Can Decentralized Learning be more robust than Federated Learning?	Mar 7, 2023	Federated Learning	—Unverified
Can Fair Federated Learning reduce the need for Personalisation?	May 4, 2023	Federated Learning	—Unverified
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning	Apr 17, 2025	Federated LearningModel Poisoning	—Unverified
Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution	Nov 5, 2020	Federated LearningManagement	—Unverified
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression	Jul 20, 2021	Distributed OptimizationFederated Learning	—Unverified
Canoe : A System for Collaborative Learning for Neural Nets	Aug 27, 2021	Edge-computingFederated Learning	—Unverified
Can Public Large Language Models Help Private Cross-device Federated Learning?	May 20, 2023	Federated Learning	—Unverified
Collaborative Content Moderation in the Fediverse	Jan 10, 2025	Federated Learning	—Unverified
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?	Sep 5, 2024	Federated Learning	—Unverified
Can We Trust the Similarity Measurement in Federated Learning?	Oct 20, 2023	Federated LearningModel Poisoning	—Unverified
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning	Sep 30, 2023	Federated LearningModel Compression	—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