Federated Learning Framework

Federated learning (FL) frameworks enable collaborative training of machine learning models on decentralized datasets without directly sharing sensitive data, addressing privacy concerns in various domains like healthcare and finance. Current research emphasizes improving FL's efficiency and robustness by addressing data heterogeneity, communication bottlenecks, and privacy vulnerabilities through techniques like differential privacy, adaptive aggregation weights, and specialized algorithms for different model architectures (e.g., convolutional neural networks, XGBoost, large language models). These advancements are significant for facilitating large-scale collaborative machine learning while upholding data privacy and security, impacting both scientific research and practical applications across numerous sectors.

Papers

November 7, 2024

FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation
Liangrui Pan, Mao Huang, Lian Wang, Pinle Qin, Shaoliang Peng
LeArning Abstract Differential Privacy Practical Method Image Segmentation Medical Image Data Deep Learning Technology Federated Learning Framework

October 15, 2024

Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study
Oscar Torres Sanchez, Guilherme Borges, Duarte Raposo, André Rodrigues, Fernando Boavida, Jorge Sá Silva
Case Study Federated Learning Framework Industrial Internet of Thing Anomaly Detection Model Industrial Internet Federated Foundation Model

September 20, 2024

Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework
Claudio Bonesana, Daniele Malpetti, Sandra Mitrović, Francesca Mangili, Laura Azzimonti
Machine Learning Model Federated Learning Sensitive Data Multi Party Federated Learning Framework

September 17, 2024

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Zilinghan Li, Shilan He, Ze Yang, Minseok Ryu, Kibaek Kim, Ravi Madduri
Federated Learning Privacy Preservation Federated Learning Framework Extensible Framework

September 10, 2024

Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air
Seyed Mohammad Azimi-Abarghouyi, Lav R. Varshney
Federated Learning Air Computation Joint Source Channel Coding Federated Learning Framework

September 7, 2024

FedModule: A Modular Federated Learning Framework
Chuyi Chen, Zhe Zhang, Yanchao Zhao
Federated Learning Federated Learning Framework

August 17, 2024

FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models
Xiaochen Wang, Jiaqi Wang, Houping Xiao, Jinghui Chen, Fenglong Ma
Foundation Model Knowledge Injection Federated Learning Framework Clinical Task

August 16, 2024

A Multivocal Literature Review on Privacy and Fairness in Federated Learning
Beatrice Balbierer, Lukas Heinlein, Domenique Zipperling, Niklas Kühl
Differential Privacy Procedural Fairness Privacy Policy Federated Learning Framework

August 8, 2024

Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
Wei Huo, Changxin Liu, Kemi Ding, Karl Henrik Johansson, Ling Shi
Private Federated Learning Differential Privacy Guarantee Federated Learning Framework Newton Method

June 21, 2024

Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
JiaYing Zheng, HaiNan Zhang, LingXiang Wang, WangJie Qiu, HongWei Zheng, ZhiMing Zheng
Large Language Model LeArning Abstract Federated Learning Private Data Federated Learning Framework

May 6, 2024

Federated Learning for Drowsiness Detection in Connected Vehicles
William Lindskog, Valentin Spannagl, Christian Prehofer
Federated Learning Framework Connected Vehicle Drowsiness Detection Driver Drowsiness Detection Driver Monitoring Driver Monitoring System

May 2, 2024

Gradient-Congruity Guided Federated Sparse Training
Chris Xing Tian, Yibing Liu, Haoliang Li, Ray C. C. Cheung, Shiqi Wang
Sparse Training Federated Learning Framework Gradient Alignment Sparse Federated

April 15, 2024

Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection
Lisang Zhou, Meng Wang, Ning Zhou
Deep Learning Model Medical Image Analysis Medical Image Classification Federated Prompt Cooperation Brain Tumor Federated Learning Framework Large Scale Medical

March 22, 2024

Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
Chengxi Li, Ming Xiao, Mikael Skoglund
Differential Privacy Mutual Information Federated Learning Framework Straggler Mitigation Aggregated Gradient Adaptive Federated Learning

March 1, 2024

Cloud-based Federated Learning Framework for MRI Segmentation
Rukesh Prajapati, Amr S. El-Wakeel
Deep Reinforcement Learning Federated Learning Framework Brain Image Magnetic Resonance Imaging Segmentation Brain Tissue Segmentation

February 22, 2024

Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs
Anika Hannemann, Jan Ewald, Leo Seeger, Erik Buchmann
Machine Learning Data Heterogeneity Trade Offs Federated Learning Framework Transcriptomic Data Model Quality

January 16, 2024

Learn What You Need in Personalized Federated Learning
Kexin Lv, Rui Ye, Xiaolin Huang, Jie Yang, Siheng Chen
Personalized Federated Learning Local Model Learnable Parameter Federated Learning Framework

December 21, 2023

Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
Guangyin Bao, Qi Zhang, Duoqian Miao, Zixuan Gong, Liang Hu, Ke Liu, Yang Liu, Chongyang Shi
Federated Learning Multi Modal Modality Specific Missing Modality Federated Learning Framework Fusion Module Prototype Mask

November 23, 2023

Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning
Abhishek Sebastian, Pragna R, Sudhakaran G, Renjith P N, Leela Karthikeyan H
Intrusion Detection Vehicle Plan Decentralized Machine Learning Federated Learning Framework

November 9, 2023

The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
Elaheh Jafarigol, Theodore Trafalis
Deep Learning Strong Generalization Industrial Disturbing Noise Privacy Policy Federated Learning Framework Additive Noise Noise Injection

Federated Learning Framework

Papers

FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation

Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study

Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework

Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air

FedModule: A Modular Federated Learning Framework

FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models

A Multivocal Literature Review on Privacy and Fairness in Federated Learning

Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

Safely Learning with Private Data: A Federated Learning Framework for Large Language Model

Federated Learning for Drowsiness Detection in Connected Vehicles

Gradient-Congruity Guided Federated Sparse Training

Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection

Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation

Cloud-based Federated Learning Framework for MRI Segmentation

Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs

Learn What You Need in Personalized Federated Learning

Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast

Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning

The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning