Decentralized Machine Learning

Decentralized machine learning aims to train machine learning models collaboratively across multiple devices without centralizing data, prioritizing data privacy and security. Current research heavily focuses on improving communication efficiency through techniques like model compression and optimized aggregation strategies, addressing challenges posed by non-independent and identically distributed (non-IID) data and unreliable participants. This field is significant for enabling large-scale machine learning in privacy-sensitive applications like healthcare and IoT, while also advancing fundamental understanding of distributed optimization and robust algorithms.