Federated Learning
Federated learning (FL) is a decentralized machine learning approach enabling collaborative model training across multiple devices without directly sharing their data, thereby preserving privacy. Current research focuses on addressing challenges like data heterogeneity (non-IID data), communication efficiency (e.g., using scalar updates or spiking neural networks), and robustness to adversarial attacks or concept drift, often employing techniques such as knowledge distillation, James-Stein estimators, and adaptive client selection. FL's significance lies in its potential to unlock the power of massive, distributed datasets for training sophisticated models while adhering to privacy regulations and ethical considerations, with applications spanning healthcare, IoT, and other sensitive domains.
Papers
COALA: A Practical and Vision-Centric Federated Learning Platform
Weiming Zhuang, Jian Xu, Chen Chen, Jingtao Li, Lingjuan Lyu
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
Xinghao Wu, Jianwei Niu, Xuefeng Liu, Mingjia Shi, Guogang Zhu, Shaojie Tang
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training
Ye Lin Tun, Chu Myaet Thwal, Minh N. H. Nguyen, Choong Seon Hong
Poisoning with A Pill: Circumventing Detection in Federated Learning
Hanxi Guo, Hao Wang, Tao Song, Tianhang Zheng, Yang Hua, Haibing Guan, Xiangyu Zhang
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models
Shanghao Shi, Md Shahedul Haque, Abhijeet Parida, Marius George Linguraru, Y. Thomas Hou, Syed Muhammad Anwar, Wenjing Lou
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator
Thinh Nguyen, Khoa D Doan, Binh T. Nguyen, Danh Le-Phuoc, Kok-Seng Wong
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging
Kumail Alhamoud, Yasir Ghunaim, Motasem Alfarra, Thomas Hartvigsen, Philip Torr, Bernard Ghanem, Adel Bibi, Marzyeh Ghassemi
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities
Pranav Poudel, Prashant Shrestha, Sanskar Amgain, Yash Raj Shrestha, Prashnna Gyawali, Binod Bhattarai
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park, Sungha Choi, Simyung Chang, Sungrack Yun