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 176–200 of 6771 papers

Title	Date	Tasks	Status	Hype
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning	Oct 15, 2021	Federated Learning	CodeCode Available	1
Acceleration of Federated Learning with Alleviated Forgetting in Local Training	Mar 5, 2022	Distributed OptimizationFederated Learning	CodeCode Available	1
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data	Jul 15, 2022	Federated LearningModel Compression	CodeCode Available	1
Adaptive Test-Time Personalization for Federated Learning	Oct 28, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter	May 21, 2023	ClusteringFederated Learning	CodeCode Available	1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training	Jun 3, 2024	Distributed OptimizationFederated Learning	CodeCode Available	1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification	Jan 20, 2025	Federated Learningimage-classification	CodeCode Available	1
Accumulative Poisoning Attacks on Real-time Data	Jun 18, 2021	Federated Learning	CodeCode Available	1
Adaptive Federated Optimization	Feb 29, 2020	Federated Learning	CodeCode Available	1
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum	Nov 30, 2023	Federated Learning	CodeCode Available	1
Communication-Efficient Learning of Deep Networks from Decentralized Data	Feb 17, 2016	Federated LearningSpeech Recognition	CodeCode Available	1
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning	Feb 6, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Continual Local Training for Better Initialization of Federated Models	May 26, 2020	Federated Learning	CodeCode Available	1
Analysis and Evaluation of Synchronous and Asynchronous FLchain	Dec 15, 2021	Federated Learning	CodeCode Available	1
Data-Free Knowledge Distillation for Heterogeneous Federated Learning	May 20, 2021	Data-free Knowledge DistillationFederated Learning	CodeCode Available	1
TabLeak: Tabular Data Leakage in Federated Learning	Oct 4, 2022	Federated LearningReconstruction Attack	CodeCode Available	1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning	Dec 16, 2020	Federated LearningIntrusion Detection	CodeCode Available	1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	Mar 10, 2021	Federated Learning	CodeCode Available	1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations	Aug 21, 2023	Federated Learning	CodeCode Available	1
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning	Jun 19, 2020	Federated LearningStochastic Optimization	CodeCode Available	1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges	Nov 15, 2022	Federated Learning	CodeCode Available	1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing	Jul 20, 2023	Federated LearningOne-Shot Learning	CodeCode Available	1
DeceFL: A Principled Decentralized Federated Learning Framework	Jul 15, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated 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