Build Collaborative Model for Privacy Preserving Using Federated Learning

Federated learning (FL), a privacy-preserving machine learning paradigm that trains a shared model by multiple entities without sharing raw data, is a perfect distributed strategy to enhance security and minimize the risk of data breaches in privacy-sensitive applications. This paper also explores FL designs (i.e., E. analyzes the security issues, data heterogeneity, and communication overhead (horizontal and vertical FL). Experimental results show that the proposed FL framework can obtain 92.3 percent accuracy in spatial prediction and 89.7 percent accuracy in temporal modeling, can reduce training time by 30 percent, and can decrease the risk of data leakage by 95 percent with encryption-based safe aggregation. FL is a promising solution for privacy-preserving machine learning in real-world applications and is particularly useful in industries such as finance, healthcare and Internet of Things (IoT).