- Nice post
wissem.dmcenter | 1 year, 5 months ago
The potential of machine learning has grown significantly over the last decade following the improvements in computational power. However, to achieve accurate machine learning solutions, we need both complex architectures and enough data to feed it. Centralized solutions, where data is accumulated from different sources and stored on the central server to find a global model, require even more computational power due to exponentially increased parameter numbers. On the other hand, distributed solutions across multiple users can decrease the one big solution into small parts without raising data storage constraints.
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