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 2526–2550 of 6771 papers

Title	Date	Tasks	Status
Buffered Asynchronous SGD for Byzantine Learning	Mar 2, 2020	Edge-computingFederated Learning	—Unverified
Detection and Prevention Against Poisoning Attacks in Federated Learning	Oct 24, 2022	Federated Learning	—Unverified
Detailed comparison of communication efficiency of split learning and federated learning	Sep 18, 2019	Federated Learning	—Unverified
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning	May 9, 2023	Bayesian OptimizationFederated Learning	—Unverified
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach	Apr 21, 2025	Federated Learning	—Unverified
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging	Jun 6, 2023	Federated Learning	—Unverified
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm	Mar 19, 2022	Federated LearningVertical Federated Learning	—Unverified
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning	Apr 9, 2023	Federated LearningVocal Bursts Valence Prediction	—Unverified
Bandwidth Slicing to Boost Federated Learning in Edge Computing	Oct 24, 2019	Edge-computingFederated Learning	—Unverified
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM	Feb 25, 2025	Computational EfficiencyFederated Learning	—Unverified
Design and Analysis of Uplink and Downlink Communications for Federated Learning	Dec 7, 2020	Federated LearningQuantization	—Unverified
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning	Aug 27, 2024	Data CompressionFederated Learning	—Unverified
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection	Feb 17, 2021	Event DetectionFederated Learning	—Unverified
DER Forecast using Privacy Preserving Federated Learning	Jul 7, 2021	Federated LearningPrediction	—Unverified
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks	Jan 10, 2021	FairnessFederated Learning	—Unverified
DepthFL: Depthwise Federated Learning for Heterogeneous Clients	May 1, 2023	Federated Learning	—Unverified
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning	Apr 12, 2025	Federated LearningQuantization	—Unverified
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning	Dec 14, 2020	FairnessFederated Learning	—Unverified
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV	Jul 22, 2024	class-incremental learningClass Incremental Learning	—Unverified
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach	Feb 15, 2023	Federated LearningMulti-agent Reinforcement Learning	—Unverified
Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent	Oct 7, 2022	Federated Learning	—Unverified
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things	Jan 3, 2025	DenoisingFederated Learning	—Unverified
Banded Square Root Matrix Factorization for Differentially Private Model Training	May 22, 2024	Federated Learning	—Unverified
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning	Apr 21, 2023	Federated LearningModel Poisoning	—Unverified
Demystifying the Effects of Non-Independence in Federated Learning	Mar 20, 2021	FairnessFederated Learning	—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