Distributed Deep Learning Training

11.

Advanced Topics and Future Directions

11.1.

Federated Learning

11.1.1.

Distributed Training Across Organizations

11.1.2.

Privacy-Preserving Techniques

11.1.3.

Communication Efficiency

11.2.

Elastic Training

11.2.1.

Dynamic Resource Scaling

11.2.2.

Fault Tolerance

11.2.3.

Resource Efficiency

11.3.

Heterogeneous Training

11.3.1.

Mixed Hardware Configurations

11.3.2.

Adaptive Parallelism

11.3.3.

Resource-Aware Scheduling

11.4.

Communication-Efficient Training

11.4.1.

Gradient Compression Advances

11.4.2.

Decentralized Learning

11.4.3.

Local Update Methods

11.5.

Large-Scale Training Challenges

11.5.1.

Extreme-Scale Parallelism

11.5.2.

Communication Bottlenecks

11.5.3.

Convergence at Scale

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10. Practical Implementation

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1. Introduction to Distributed Deep Learning