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
Data Poisoning and Leakage Analysis in Federated Learning
Wenqi Wei, Tiansheng Huang, Zachary Yahn, Anoop Singhal, Margaret Loper, Ling Liu
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
Manh V. Nguyen, Liang Zhao, Bobin Deng, William Severa, Honghui Xu, Shaoen Wu
Green Federated Learning: A new era of Green Aware AI
Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino
Some Results on Neural Network Stability, Consistency, and Convergence: Insights into Non-IID Data, High-Dimensional Settings, and Physics-Informed Neural Networks
Ronald Katende, Henry Kasumba, Godwin Kakuba, John M. Mango
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks
Tran Viet Khoa, Mohammad Abu Alsheikh, Yibeltal Alem, Dinh Thai Hoang
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation
Tiantian Feng, Tuo Zhang, Salman Avestimehr, Shrikanth S. Narayanan
Convergent Differential Privacy Analysis for General Federated Learning: the $f$-DP Perspective
Yan Sun, Li Shen, Dacheng Tao
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization
Qianyi Zhao, Chen Qu, Cen Chen, Mingyuan Fan, Yanhao Wang
Submodular Maximization Approaches for Equitable Client Selection in Federated Learning
Andrés Catalino Castillo Jiménez, Ege C. Kaya, Lintao Ye, Abolfazl Hashemi
Towards Case-based Interpretability for Medical Federated Learning
Laura Latorre, Liliana Petrychenko, Regina Beets-Tan, Taisiya Kopytova, Wilson Silva