SAN Storage at the Edge of AI
Federated learning is reshaping how artificial intelligence models are trained, particularly in environments where data privacy and sovereignty are paramount. Instead of centralizing massive datasets, this distributed machine learning approach trains models locally on edge devices. This method offers significant benefits, including reduced data transmission costs and enhanced privacy, as raw data never leaves its source. However, as AI models become more complex, the storage infrastructure at the edge faces unprecedented demands. The need for high-performance, scalable, and reliable data storage has brought traditional enterprise solutions like Storage Area Networks (SANs) into the conversation about edge computing. This post will explore the role of SAN storage in supporting federated learning at the edge. We will cover the fundamentals of SANs, evaluate their benefits and challenges in edge deployments, and look at real-world applications where this combination is driving in...