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Answer: Gradient aggregation, where each node computes gradients locally and only the aggregated gradients are communicated, significantly reducing the amount of data transferred.
**Correct Option: C. Gradient Aggregation** Gradient aggregation is the most effective technique for minimizing communication overhead in a distributed machine learning system, especially under the given constraints. It allows each node to compute gradients locally and only communicates the aggregated gradients, thus significantly reducing the amount of data transferred across the network. This approach is particularly beneficial for real-time model updates and adheres to network bandwidth and data privacy regulations. **Why other options are less effective:** - **A. Data replication**: While it enhances fault tolerance and data availability, it can increase communication overhead, especially when dealing with petabytes of data across multiple data centers. - **B. Model compression**: Although it reduces the model size, it does not directly tackle the communication overhead during the training process. - **D. Data sharding**: Distributes the dataset across nodes but does not inherently reduce the communication overhead during model training. - **E. None of the above**: Gradient aggregation effectively addresses the requirement, making this option incorrect.
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In the context of optimizing a distributed machine learning system for a global e-commerce platform, where the system is required to process petabytes of data across multiple data centers with minimal latency and cost, which of the following techniques is MOST effective for minimizing communication overhead between nodes? Consider the need for real-time model updates and the constraints of network bandwidth and data privacy regulations. Choose the best option.
A
Data replication across all nodes to ensure high availability and fault tolerance, despite the potential increase in communication overhead.
B
Model compression techniques such as pruning and quantization to reduce the size of the model, without directly addressing the communication overhead.
C
Gradient aggregation, where each node computes gradients locally and only the aggregated gradients are communicated, significantly reducing the amount of data transferred.
D
Data sharding to distribute the dataset across nodes, which does not inherently reduce the communication overhead during model training.
E
None of the above options fully address the requirement for minimizing communication overhead while considering all given constraints.