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 5901–5950 of 6771 papers

Title	Date	Tasks	Status
Communication-Efficient Federated Learning with Sketching	Jan 1, 2020	Federated Learning	—Unverified
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks	Jul 20, 2023	Federated LearningQuantization	—Unverified
Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM	Jan 1, 2024	Federated Learning	—Unverified
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth	May 6, 2024	Federated LearningModel Compression	—Unverified
Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching	Oct 30, 2024	Federated Learning	—Unverified
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data	Mar 20, 2025	Distributed OptimizationFederated Learning	—Unverified
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression	Apr 26, 2021	Federated LearningLow-rank compression	—Unverified
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks	Jan 20, 2025	Federated LearningQuantization	—Unverified
Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization	Sep 19, 2024	Federated Learning	—Unverified
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission	Aug 7, 2023	Federated LearningSemantic Communication	—Unverified
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning	Apr 15, 2024	Federated LearningVertical Federated Learning	—Unverified
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory	Mar 9, 2020	Federated Learning	—Unverified
Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments	Jul 8, 2025	Federated LearningPrivacy Preserving	—Unverified
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection	Jan 30, 2024	Federated Learning	—Unverified
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data	Nov 28, 2018	BIG-bench Machine LearningFederated Learning	—Unverified
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks	Jan 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets	Jun 17, 2020	Federated Learning	—Unverified
Communication-Efficient Robust Federated Learning with Noisy Labels	Jun 11, 2022	Bilevel OptimizationFederated Learning	—Unverified
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples	Mar 28, 2023	Federated LearningVertical Federated Learning	—Unverified
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models	May 1, 2025	Federated Learning	—Unverified
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks	Apr 16, 2025	Computational EfficiencyFederated Learning	—Unverified
Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates	Aug 12, 2021	DecoderFederated Learning	—Unverified
Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation	Aug 15, 2024	Federated LearningScheduling	—Unverified
Federated Learning of Neural ODE Models with Different Iteration Counts	Aug 19, 2022	Federated Learning	—Unverified
Communication Trade-offs in Federated Learning of Spiking Neural Networks	Feb 27, 2023	Federated Learning	—Unverified
CommunityAI: Towards Community-based Federated Learning	Nov 29, 2023	Federated Learning	—Unverified
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems	Jun 15, 2023	Community DetectionFederated Learning	—Unverified
Comparative assessment of federated and centralized machine learning	Feb 3, 2022	BIG-bench Machine LearningFederated Learning	—Unverified
Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service	Mar 15, 2023	Federated LearningManagement	—Unverified
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data	Dec 1, 2021	Federated Learning	—Unverified
Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay	Jul 17, 2024	Federated Learning	—Unverified
Comparing privacy notions for protection against reconstruction attacks in machine learning	Feb 6, 2025	Federated Learning	—Unverified
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare	Dec 23, 2020	Binary ClassificationClassification	—Unverified
Competitive Advantage Attacks to Decentralized Federated Learning	Oct 20, 2023	Federated Learning	—Unverified
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning	Mar 10, 2023	Federated LearningPrivacy Preserving	—Unverified
Completely Heterogeneous Federated Learning	Oct 28, 2022	Data-free Knowledge DistillationFederated Learning	—Unverified
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission	Jun 3, 2022	Federated LearningQuantization	—Unverified
Complex-valued Federated Learning with Differential Privacy and MRI Applications	Oct 7, 2021	Federated LearningMRI Reconstruction	—Unverified
Composite federated learning with heterogeneous data	Sep 4, 2023	Federated Learning	—Unverified
Compositional federated learning: Applications in distributionally robust averaging and meta learning	Jun 21, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning	Aug 2, 2023	Federated Learningregression	—Unverified
Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT	May 9, 2024	Federated Learning	—Unverified
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data	Jun 16, 2022	Federated LearningQuantization	—Unverified
Compression Boosts Differentially Private Federated Learning	Nov 10, 2020	Compressive SensingFederated Learning	—Unverified
Compression with Exact Error Distribution for Federated Learning	Oct 31, 2023	Federated Learning	—Unverified
Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks	Sep 4, 2023	Federated Learning	—Unverified
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT	Mar 30, 2024	Computational EfficiencyFederated Learning	—Unverified
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles	Jun 3, 2025	Edge-computingFederated Learning	—Unverified
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach	Jun 13, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air	Sep 10, 2024	Federated 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