Federated Machine Learning

Federated machine learning (FML) is a distributed machine learning approach enabling collaborative model training across multiple devices or institutions without directly sharing sensitive data. Current research emphasizes addressing challenges like data heterogeneity, improving efficiency (especially for energy-constrained devices), and enhancing privacy through techniques such as differential privacy and secure multi-party computation, often employing models like support vector machines, XGBoost, and spiking neural networks. FML's significance lies in its potential to unlock the value of decentralized data for various applications, particularly in healthcare and other privacy-sensitive domains, while mitigating data security and privacy risks.