Distributed Deep Learning Training

  1. Advanced Topics and Future Directions
    1. Federated Learning
      1. Distributed Training Across Organizations
        1. Privacy-Preserving Techniques
          1. Communication Efficiency
          2. Elastic Training
            1. Dynamic Resource Scaling
              1. Fault Tolerance
                1. Resource Efficiency
                2. Heterogeneous Training
                  1. Mixed Hardware Configurations
                    1. Adaptive Parallelism
                      1. Resource-Aware Scheduling
                      2. Communication-Efficient Training
                        1. Gradient Compression Advances
                          1. Decentralized Learning
                            1. Local Update Methods
                            2. Large-Scale Training Challenges
                              1. Extreme-Scale Parallelism
                                1. Communication Bottlenecks
                                  1. Convergence at Scale

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                                10. Practical Implementation

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                                1. Introduction to Distributed Deep Learning