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6 - Efficient Training with Heterogeneous Data Distribution
Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
Book:

Edge Learning for Distributed Big Data Analytics

Published online:

14 January 2022

Print publication:

10 February 2022, pp 98-111
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Summary

In Edge Learning, training data are non-independently and identically distributed (non-IID). Applying the same learning strategy for all workers fails to work efficiently. In this chapter, we introduce federated learning, where the training data are always non-IID due to data isolation. Then, we summarize enabling technologies for efficient training with non-IID data. We also propose a reinforcement learning-based method that takes non-IID property and resource-constrain into consideration and adjusts the hyper-parameters to accelerate the loss descent efficiency.