H13-311_V3.5 · Question #86
On the premise of ensuring data privacy and security, federated learning utilizes different data sources to cooperatively train models to improve breakthroughs in data bottlenecks.
The correct answer is A. TRUE. A is correct because federated learning is specifically designed to enable collaborative model training across multiple distributed data sources (devices, institutions, etc.) without centralizing or sharing raw data - privacy is a core architectural guarantee, not an…
Question
On the premise of ensuring data privacy and security, federated learning utilizes different data sources to cooperatively train models to improve breakthroughs in data bottlenecks.
Options
- ATRUE
- BFALSE
How the community answered
(25 responses)- A76% (19)
- B24% (6)
Explanation
A is correct because federated learning is specifically designed to enable collaborative model training across multiple distributed data sources (devices, institutions, etc.) without centralizing or sharing raw data - privacy is a core architectural guarantee, not an afterthought. The technique directly addresses the "data bottleneck" problem by allowing participants who cannot share sensitive data (e.g., hospitals, banks) to still contribute to a shared model.
B is incorrect because it would mean the statement is false, but the statement accurately describes federated learning's fundamental purpose and design.
Memory tip: Think of "federated" as in a federation of independent parties - like separate countries cooperating while keeping their internal affairs private. Each party trains locally and only shares model updates (gradients), never raw data.
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